# DecryptPrompt > 如果LLM的突然到来让你感到沮丧，不妨读下主目录的Choose Your Weapon Survival Strategies for Depressed AI Academics 持续更新以下内容，Star to keep updated~ ## LLM资源汇总 - [开源模型和评测榜单](开源模型.MD) - [开源推理，微调，Agent，RAG，propmt 框架](开源框架.MD) - [开源SFT，RLHF，Pretrain 数据集](开源数据.MD) - [AIGC各领域应用汇总](AIGC各领域应用.MD) - [Prompt教程，经典博客和AI会议访谈](教程博客会议.MD) ## 跟着博客读论文 - [解密Prompt系列1. Tunning-Free Prompt：GPT2 & GPT3 & LAMA & AutoPrompt](https://cloud.tencent.com/developer/article/2215545?areaSource=&traceId=) - [解密Prompt系列2. 冻结Prompt微调LM： T5 & PET & LM-BFF](https://cloud.tencent.com/developer/article/2223355?areaSource=&traceId=) - [解密Prompt系列3. 冻结LM微调Prompt: Prefix-tuning & Prompt-tuning & P-tuning](https://cloud.tencent.com/developer/article/2237259?areaSource=&traceId=) - [解密Prompt系列4. 升级Instruction Tuning：Flan/T0/InstructGPT/TKInstruct](https://cloud.tencent.com/developer/article/2245094?areaSource=&traceId=) - [解密prompt系列5. APE+SELF=自动化指令集构建代码实现](https://cloud.tencent.com/developer/article/2260697?areaSource=&traceId=) - [解密Prompt系列6. lora指令微调扣细节-请冷静,1个小时真不够~](https://cloud.tencent.com/developer/article/2276508) - [解密Prompt系列7. 偏好对齐RLHF-OpenAI·DeepMind·Anthropic对比分析](https://cloud.tencent.com/developer/article/old/2289566?areaSource=&traceId=) - [解密Prompt系列8. 无需训练让LLM支持超长输入:知识库 & Unlimiformer & PCW & NBCE ](https://cloud.tencent.com/developer/article/old/2295783?areaSource=&traceId=) - [解密Prompt系列9. COT：模型复杂推理-思维链基础和进阶玩法](https://cloud.tencent.com/developer/article/old/2296079?areaSource=&traceId=) - [解密Prompt系列10. COT：思维链COT原理探究](https://cloud.tencent.com/developer/article/old/2298660) - [解密Prompt系列11. COT：小模型也能COT，先天不足后天补](https://cloud.tencent.com/developer/article/old/2301999) - [解密Prompt系列12. LLM Agent零微调范式 ReAct & Self Ask](https://cloud.tencent.com/developer/article/2305421) - [解密Prompt系列13. LLM Agent指令微调方案: Toolformer & Gorilla](https://cloud.tencent.com/developer/article/2312674) - [解密Prompt系列14. LLM Agent之搜索应用设计：WebGPT & WebGLM & WebCPM](https://cloud.tencent.com/developer/article/2319879) - [解密Prompt系列15. LLM Agent之数据库应用设计：DIN & C3 & SQL-Palm & BIRD](https://cloud.tencent.com/developer/article/2328749) - [解密Prompt系列16. LLM对齐经验之数据越少越好？LTD & LIMA & AlpaGasus](https://cloud.tencent.com/developer/article/2333495) - [解密Prompt系列17. LLM对齐方案再升级 WizardLM & BackTranslation & SELF-ALIGN](https://cloud.tencent.com/developer/article/2338592) - [解密Prompt系列18. LLM Agent之只有智能体的世界](https://cloud.tencent.com/developer/article/2351540) - [解密Prompt系列19. LLM Agent之数据分析领域的应用：Data-Copilot & InsightPilot](https://cloud.tencent.com/developer/article/2358413) - [解密Prompt系列20. RAG之再谈召回多样性优化](https://cloud.tencent.com/developer/article/2365050) - [解密Prompt系列21. RAG之再谈召回信息密度和质量](https://cloud.tencent.com/developer/article/2369977) - [​解密Prompt系列22. RAG的反思：放弃了压缩还是智能么？](https://cloud.tencent.com/developer/article/2375066) - [解密Prompt系列23.大模型幻觉分类&归因&检测&缓解方案脑图全梳理](https://cloud.tencent.com/developer/article/2378383) - [解密prompt系列24. RLHF新方案之训练策略：SLiC-HF & DPO & RRHF & RSO](https://cloud.tencent.com/developer/article/2389619) - [解密prompt系列25. RLHF改良方案之样本标注：RLAIF & SALMON](https://cloud.tencent.com/developer/article/2398654) - [解密prompt系列26. 人类思考vs模型思考：抽象和发散思维](https://cloud.tencent.com/developer/article/2394120) - [解密prompt系列27. LLM对齐经验之如何降低通用能力损失](https://cloud.tencent.com/developer/article/2406888) - [解密Prompt系列28. LLM Agent之金融领域智能体：FinMem & FinAgent](https://cloud.tencent.com/developer/article/2411792) - [解密Prompt系列29. LLM Agent之真实世界海量API解决方案：ToolLLM & AnyTool](https://cloud.tencent.com/developer/article/2415908) - [解密Prompt系列30. LLM Agent之互联网冲浪智能体们](https://cloud.tencent.com/developer/article/2419768) - [​解密Prompt系列31. LLM Agent之从经验中不断学习的智能体](https://cloud.tencent.com/developer/article/2425139) - [解密Prompt系列32. LLM之表格理解任务-文本模态](https://cloud.tencent.com/developer/article/2429900) - [解密Prompt系列33. LLM之图表理解任务-多模态篇](https://cloud.tencent.com/developer/article/2433883) - [​解密prompt系列34. RLHF之训练另辟蹊径：循序渐进 & 青出于蓝](https://cloud.tencent.com/developer/article/2437031) - [解密prompt系列35. Prompt标准化进行时！ DSPy论文串烧和代码示例](https://cloud.tencent.com/developer/article/2441201) - [解密Prompt系列36. Prompt结构化编写和最优化算法UNIPROMPT](https://cloud.tencent.com/developer/article/2444167) - [解密Prompt系列37. RAG之前置决策何时联网的多种策略](https://cloud.tencent.com/developer/article/2448156) - [解密Prompt系列38. 多Agent路由策略](https://cloud.tencent.com/developer/article/2451000) - [解密prompt系列39. RAG之借助LLM优化精排环节](https://cloud.tencent.com/developer/article/2453693) - [解密prompt系列40. LLM推理scaling Law](https://cloud.tencent.com/developer/article/2456441) - [解密prompt系列41. GraphRAG真的是Silver Bullet？](https://cloud.tencent.com/developer/article/2461325) - [解密prompt系列42. LLM通往动态复杂思维链之路](https://cloud.tencent.com/developer/article/2464011) - [解密prompt系列43. LLM Self Critics](https://cloud.tencent.com/developer/article/2468406) - [解密prompt系列44. RAG探索模式？深度思考模式？](https://cloud.tencent.com/developer/article/2474048) - [解密Prompt系列45. 再探LLM Scalable Oversight -辩论、博弈哪家强](https://cloud.tencent.com/developer/article/2479401) - [解密prompt系列46. LLM结构化输出代码示例和原理分析](https://cloud.tencent.com/developer/article/2483500) - [解密prompt系列47. O1 Long Thought的一些特征分析](https://cloud.tencent.com/developer/article/2487221) - [​解密prompt系列48. DeepSeek R1 & Kimi 1.5长思维链 - RL Scaling](https://cloud.tencent.com/developer/article/2493924) - [​解密prompt系列49. 回顾R1之前的思维链发展](https://cloud.tencent.com/developer/article/2497501) - [解密prompt系列50. RL用于优化Agent行为路径的一些思路](https://cloud.tencent.com/developer/article/2502322) - [解密prompt系列51. R1实验的一些细节讨论](https://cloud.tencent.com/developer/article/2506684) - [解密prompt系列52. 闲聊大模型还有什么值得探索的领域](https://cloud.tencent.com/developer/article/2510004) - [解密prompt系列53. 再谈大模型Memory](https://cloud.tencent.com/developer/article/2514545) - [解密prompt系列54. Context Cache代码示例和原理分析](https://cloud.tencent.com/developer/article/2522820) - [解密prompt系列55. Agent Memory的工程实现 - Mem0 & LlamaIndex](https://cloud.tencent.com/developer/article/2528447) - [解密prompt系列56. Agent context Engineering - 单智能体代码剖析](https://cloud.tencent.com/developer/article/2537040) - [​解密prompt系列57. Agent Context Engineering - 多智能体代码剖析](https://cloud.tencent.com/developer/article/2541926) - [解密prompt系列58. MCP - 工具演变 & MCP基础](https://cloud.tencent.com/developer/article/2549927) - [解密prompt系列59. MCP实战：从Low-Level到FastMCP的搭建演进](https://cloud.tencent.com/developer/article/2554794) - [​解密prompt系列60. Agent实战：从0搭建Jupter数据分析智能体](https://cloud.tencent.com/developer/article/2563549) - [​解密prompt系列61. 手搓代码沙箱与FastAPI-MCP实战](https://cloud.tencent.com/developer/article/2570796) - [​解密prompt系列62. Agent Memory新视角 - MATTS&CFGM&MIRIX](https://cloud.tencent.com/developer/article/2577365) - [解密prompt系列63. Agent训练方案: RStar2 & Early Experience etc](https://cloud.tencent.com/developer/article/2581959) - [解密Prompt系列64. Anthropic Skils的延伸思考](https://cloud.tencent.com/developer/article/2586667) - [解密Prompt系列65. 三巨头关于大模型内景的硬核论文](https://cloud.tencent.com/developer/article/2594738) - [解密Prompt系列66. 视觉Token爆炸→DeepSeek-OCR光学压缩](https://cloud.tencent.com/developer/article/2600104) - [解密Prompt系列67. 智能体的经济学：从架构选型到工具预算](https://cloud.tencent.com/developer/article/2610869) - [解密Prompt系列68. 告别逐词蹦字 - Transformer 的新推理范式](https://cloud.tencent.com/developer/article/2616180) ## 和AI一起搞事情 - [和AI一起搞事情#1: opencode ×browser-use实战复盘](https://cloud.tencent.com/developer/user/6190096) - [和AI一起搞事情#2：边剥龙虾&边做个中医方剂技能](https://cloud.tencent.com/developer/article/2642702) - [和AI一起搞事情#3：Claude Teammate 开发中医游戏翻车了](https://cloud.tencent.com/developer/article/2650411) ## 论文汇总 ### paper List - https://github.com/dongguanting/In-Context-Learning_PaperList - https://github.com/thunlp/PromptPapers - https://github.com/Timothyxxx/Chain-of-ThoughtsPapers - https://github.com/thunlp/ToolLearningPapers - https://github.com/MLGroupJLU/LLM-eval-survey - https://github.com/thu-coai/PaperForONLG - https://github.com/khuangaf/Awesome-Chart-Understanding - https://github.com/srush/awesome-o1/?tab=readme-ov-file ### 图像生成 - Neural Discrete Representation Learning - Denoising Diffusion Probabilistic Models - Scalable Diffusion Models with Transformers - Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding - High-Resolution Image Synthesis with Latent Diffusion Models ### Post Train（和COT，RL有交集） - Inference Scaling - An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models - Are More LM Calls All You Need? Towards the Scaling Properties of Compound AI Systems - Large Language Monkeys: Scaling Inference Compute with Repeated Sampling - Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters :star: - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning - Planning In Natural Language Improves LLM Search For Code Generation - ReST-MCTS∗ : LLM Self-Training via Process Reward Guided Tree Search - AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training - Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling - The Surprising Effectiveness of Test-Time Training for Abstract Reasoning - Inference Scaling for Long-Context Retrieval Augmented Generation - Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing - InfAlign: Inference-aware language model alignment - Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach - What type of inference is planning? - Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving - PROVABLE SCALING LAWS OF FEATURE EMERGENCE FROM LEARNING DYNAMICS OF GROKKING - Do Machine Learning Models Memorize or Generalize? - slow thinking COT - O1 Replication Journey: A Strategic Progress Report – Part 1 :star: - Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions - A Comparative Study on Reasoning Patterns of OpenAI's o1 Model - Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems - Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces - Training Large Language Models to Reason in a Continuous Latent Space - Beyond A∗ : Better Planning with Transformers via Search Dynamics Bootstrapping - o1-Coder: an o1 Replication for Coding - Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective - Sky-T1: Train your own O1 preview model within $450 - Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought - rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking :star: - Demystifying Long Chain-of-Thought Reasoning in LLMs - Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models - [Huggingface Open R1](https://huggingface.co/blog/open-r1/update-1) - CODEI/O: Condensing Reasoning Patterns via Code Input-Output Prediction - Training Language Models to Reason Efficiently - s1: Simple test-time scaling - Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking - ALPHAONE: Reasoning Models Thinking Slow and Fast at Test Time - O3 Related - Competitive Programming with Large Reasoning Models - RL COT原理 - SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training - Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs - Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs - All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning - Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? - Think Deep, Not Just Long:Measuring LLM Reasoning Effort via Deep-Thinking Tokens - R1 Reprodce - LogicRL: Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning - [SimpleR1](https://hkust-nlp.notion.site/simplerl-reason) - [Huggingface Open R1](https://huggingface.co/blog/open-r1/update-1) - DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models - Think Only When You Need with Large Hybrid-Reasoning Models - Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties - Skywork Open Reasoner 1 Technical Report - Learning to Reason: Training LLMs with GPT-OSS or DeepSeek R1 Reasoning Traces - RL Agent - RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning - ToolRL: Reward is All Tool Learning Needs - ReTool: Reinforcement Learning for Strategic Tool Use in LLMs - ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning - [Improving Multi-Turn Tool Use with Reinforcement Learning](https://www.bespokelabs.ai/blog/improving-multi-turn-tool-use-with-reinforcement-learning) - WebThinker: Empowering Large Reasoning Models with Deep Research Capability - Reinforcement Learning for Machine Learning Engineering Agents - AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning - rStar2-Agent: Agentic Reasoning Technical Report - The Landscape of Agentic Reinforcement Learning for LLMs: A Survey - IN-THE-FLOW AGENTIC SYSTEM OPTIMIZATION FOR EFFECTIVE PLANNING AND TOOL USE - UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning - PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold - DeepAnalyze: Agentic Large Language Models for Autonomous Data Science - Thinking with Programming Vision: Towards a Unified View for Thinking with Images - Scaling Agent Learning via Experience Synthesis - CaveAgent: Transforming LLMs into Stateful Runtime Operators - 经验学习 - Welcome to the Era of Experience - Agent Learning via Early Experience - 其他训练方式 - QWENLONG-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning - REWARDBENCH 2: Advancing Reward Model Evaluation - Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision - DiffusionNFT: Online Diffusion Reinforcement with Forward Process - EVOLUTION STRATEGIES AT SCALE: LLM FINETUNING BEYOND REINFORCEMENT LEARNING - Learning to Reason Across Parallel Samples for LLM Reasoning - PARAM∆ FOR DIRECT WEIGHT MIXING: POST-TRAIN LARGE LANGUAGE MODEL AT ZERO COST - LaSeR: Reinforcement Learning with Last-Token Self-Rewarding - The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains - RL Overview - Reinforcement Learning: An Overview - Towards a Unified View of Large Language Model Post-Training - RL数据集 - ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning ### Context Engineer - A Survey of Context Engineering for Large Language Models - Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models - Scaling Long-Horizon LLM Agent via Context-Folding - Towards a Science of Scaling Agent Systems - Budget-Aware Tool-Use Enables Effective Agent Scaling - Context Engineering 2.0 - End-to-End Test-Time Training for Long Context - Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets - Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned - Meta-Harness End-to-End Optimization of Model Harnesses - The-Complete-Guide-to-Building-Skill-for-Claude ### New Model Architecture - SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models - Less is More: Recursive Reasoning with Tiny Networks - Continuous Thought Machines - TiDAR: Think in Diffusion, Talk in Autoregression - Nested Learning: The Illusion of Deep Learning Architectures ### 主流LLMS和预训练 - GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL - PaLM: Scaling Language Modeling with Pathways - PaLM 2 Technical Report - GPT-4 Technical Report - Backpack Language Models - LLaMA: Open and Efficient Foundation Language Models - Llama 2: Open Foundation and Fine-Tuned Chat Models - Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning - OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch - Mistral 7B - Ziya2: Data-centric Learning is All LLMs Need - MEGABLOCKS: EFFICIENT SPARSE TRAINING WITH MIXTURE-OF-EXPERTS - TUTEL: ADAPTIVE MIXTURE-OF-EXPERTS AT SCALE - Phi1- Textbooks Are All You Need :star: - Phi1.5- Textbooks Are All You Need II: phi-1.5 technical report - Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone - Gemini: A Family of Highly Capable Multimodal Models - In-Context Pretraining: Language Modeling Beyond Document Boundaries - LLAMA PRO: Progressive LLaMA with Block Expansion - QWEN TECHNICAL REPORT - Fewer Truncations Improve Language Modeling - ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools - Phi-4 Technical Report - Byte Latent Transformer: Patches Scale Better Than Tokens - Qwen2.5 Technical Report - DeepSeek-V3 Technical Report - Mixtral of Experts - DeepSeek_R1 :star: - KIMI K1.5: SCALING REINFORCEMENT LEARNING WITH LLMS :star: - CWM: An Open-Weights LLM for Research on Code Generation with World Models - DeepSeek V3.2 Tech Report - DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models - ### 思维链 (prompt_chain_of_thought) - 基础&进阶用法 - 【zero-shot-COT】 Large Language Models are Zero-Shot Reasoners :star: - 【few-shot COT】 Chain of Thought Prompting Elicits Reasoning in Large Language Models :star: - 【SELF-CONSISTENCY 】IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS - 【LEAST-TO-MOST】 PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS :star: - 【TOT】Tree of Thoughts: Deliberate Problem Solving with Large Language Models :star: - 【Plan-and-Solve】 Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models - 【Verify-and-Edit】: A Knowledge-Enhanced Chain-of-Thought Framework - 【GOT】Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models - 【TOMT】Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning - 【LAMBADA】: Backward Chaining for Automated Reasoning in Natural Language - 【AOT】Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models :star: - 【GOT】Graph of Thoughts: Solving Elaborate Problems with Large Language Models :star: - 【PHP】Progressive-Hint Prompting Improves Reasoning in Large Language Models - 【HtT】LARGE LANGUAGE MODELS CAN LEARN RULES :star: - 【DIVSE】DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS - 【CogTree】From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models - 【Step-Back】Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models :star: - 【OPRO】LARGE LANGUAGE MODELS AS OPTIMIZERS :star: - 【BOT】Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models - Abstraction-of-Thought Makes Language Models Better Reasoners - 【SymbCoT】Faithful Logical Reasoning via Symbolic Chain-of-Thought - 【XOT】EVERYTHING OF THOUGHTS : DEFYING THE LAW OF PENROSE TRIANGLE FOR THOUGHT GENERATION - 【IoT】Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning - 【DOT】On the Diagram of Thought - 【ROT】Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up. - Thinking Forward and Backward: Effective Backward Planning with Large Language Models - 【KR】K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning - 【Self-Discover】SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures - 【Theory-of-Mind】HOW FAR ARE LARGE LANGUAGE MODELS FROMAGENTS WITH THEORY-OF-MIND? - 【PC-SUBQ】Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation - Reverse Thinking Makes LLMs Stronger Reasoners - Chain of Draft: Thinking Faster by Writing Less - Atom of Thoughts for Markov LLM Test-Time Scaling - 非传统COT问题分解方向 - Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks - Successive Prompting for Decomposing Complex Questions - 分领域COT [Math, Code, Tabular, QA] - Solving Quantitative Reasoning Problems with Language Models - SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS - Solving math word problems with processand outcome-based feedback - CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning - T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering - LEARNING PERFORMANCE-IMPROVING CODE EDITS - Chain of Code: Reasoning with a Language Model-Augmented Code Emulator - 原理分析 - Chain of Thought Empowers Transformers to Solve Inherently Serial Problems :star: - Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters :star: - TEXT AND PATTERNS: FOR EFFECTIVE CHAIN OF THOUGHT IT TAKES TWO TO TANGO - Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective - Large Language Models Can Be Easily Distracted by Irrelevant Context - Chain-of-Thought Reasoning Without Prompting - Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs - Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs - To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning :star: - Why think step by step? Reasoning emerges from the locality of experience - Internal Consistency and Self-Feedback in Large Language Models: A Survey :star: - Iteration Head: A Mechanistic Study of Chain-of-Thought :star: - The Impact of Reasoning Step Length on Large Language Models :star: - Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts? - Compressed Chain of Thought: Efficient Reasoning Through Dense Representations - Do LLMs Really Think Step-by-step In Implicit Reasoning? - Cognitive Foundations for Reasoning and Their Manifestation in LLMs - 小模型COT蒸馏 - Specializing Smaller Language Models towards Multi-Step Reasoning :star: - Teaching Small Language Models to Reason - Large Language Models are Reasoning Teachers - Distilling Reasoning Capabilities into Smaller Language Models - The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning - Distilling System 2 into System 1 - COT样本自动构建/选择 - AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS - Active Prompting with Chain-of-Thought for Large Language Models - COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING - COT能力学习 - Large Language Models Can Self-Improve - Training Chain-of-Thought via Latent-Variable Inference - Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking - STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning - V-STaR: Training Verifiers for Self-Taught Reasoners - THINK BEFORE YOU SPEAK: TRAINING LANGUAGE MODELS WITH PAUSE TOKENS - SELF-DIRECTED SYNTHETIC DIALOGUES AND REVISIONS TECHNICAL REPORT - COT-SELF-INSTRUCT: BUILDING HIGH-QUALITY SYNTHETIC PROMPTS FOR REASONING AND NON-REASONING TASKS - others - OlaGPT Empowering LLMs With Human-like Problem-Solving abilities - Challenging BIG-Bench tasks and whether chain-of-thought can solve them - Large Language Models are Better Reasoners with Self-Verification - ThoughtSource A central hub for large language model reasoning data - Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs ### Self-Evolution - Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents - [Alpha Evolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) - Can Large Reasoning Models Self-Train - Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO - Evolution Strategies at the Hyperscale - Guided Self-Evolving LLMs with Minimal Human Supervision ### RLHF - Deepmind - Teaching language models to support answers with verified quotes - sparrow, Improving alignment of dialogue agents via targetd human judgements :star: - STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION - Reinforced Self-Training (ReST) for Language Modeling - SLiC-HF: Sequence Likelihood Calibration with Human Feedback - CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION - REWARD DESIGN WITH LANGUAGE MODELS - Final-Answer RL Solving math word problems with processand outcome-based feedback - Solving math word problems with process- and outcome-based feedback - Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models - BOND: Aligning LLMs with Best-of-N Distillation - RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold - Generative Verifiers: Reward Modeling as Next-Token Prediction - Training Language Models to Self-Correct via Reinforcement Learning - openai - PPO: Proximal Policy Optimization Algorithms :star: - Deep Reinforcement Learning for Human Preference - Fine-Tuning Language Models from Human Preferences - learning to summarize from human feedback - InstructGPT: Training language models to follow instructions with human feedback :star: - Scaling Laws for Reward Model Over optimization :star: - WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION :star: - PRM：Let's verify step by step :star: - Training Verifiers to Solve Math Word Problems [PRM的前置依赖] - [OpenAI Super Alignment Blog](https://openai.com/blog/introducing-superalignment) - LLM Critics Help Catch LLM Bugs :star: - PROVER-VERIFIER GAMES IMPROVE LEGIBILITY OF LLM OUTPUTS - Rule Based Rewards for Language Model Safety - Self-critiquing models for assisting human evaluators - Anthropic - A General Language Assistant as a Laboratory for Alignmen - Measuring Progress on Scalable Oversight or Large Language Models - Red Teaming Language Models to Reduce Harms Methods,Scaling Behaviors and Lessons Learned - Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback :star: - Constitutional AI Harmlessness from AI Feedback :star: - Pretraining Language Models with Human Preferences - The Capacity for Moral Self-Correction in Large Language Models - Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainin - AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS - 改良方案 - RRHF: Rank Responses to Align Language Models with Human Feedback without tears - Chain of Hindsight Aligns Language Models with Feedback - AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback - RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment - RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback - Training Socially Aligned Language Models in Simulated Human Society - RAIN: Your Language Models Can Align Themselves without Finetuning - Generative Judge for Evaluating Alignment - PEERING THROUGH PREFERENCES: UNRAVELING FEEDBACK ACQUISITION FOR ALIGNING LARGE LANGUAGE MODELS - SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS - Large Language Model Unlearning :star: - ADVERSARIAL PREFERENCE OPTIMIZATION :star: - Preference Ranking Optimization for Human Alignment - A Long Way to Go: Investigating Length Correlations in RLHF - ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA - REWARD MODEL ENSEMBLES HELP MITIGATE OVEROPTIMIZATION - LEARNING OPTIMAL ADVANTAGE FROM PREFERENCES AND MISTAKING IT FOR REWARD - ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK - MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK - STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL - Shepherd: A Critic for Language Model Generation - LEARNING TO GENERATE BETTER THAN YOUR LLM - Fine-Grained Human Feedback Gives Better Rewards for Language Model Training - Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision - Direct Preference Optimization: Your Language Model is Secretly a Reward Model - HIR The Wisdom of Hindsight Makes Language Models Better Instruction Followers - Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction - A Minimaximalist Approach to Reinforcement Learning from Human Feedback - PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs - Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models - Weak-to-Strong Extrapolation Expedites Alignment - Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study - Token-level Direct Preference Optimization - SimPO: Simple Preference Optimization with a Reference-Free Reward - AUTODETECT: Towards a Unified Framework for Automated Weakness Detection in Large Language Models - META-REWARDING LANGUAGE MODELS: Self-Improving Alignment with LLM-as-a-Meta-Judge - HELPSTEER: Multi-attribute Helpfulness Dataset for STEERLM - Recursive Introspection: Teaching Language Model Agents How to Self-Improve - Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization - DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models - GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements - REFT: Reasoning with REinforced Fine-Tuning - SCPO：SELF-CONSISTENCY PREFERENCE OPTIMIZATION - MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking - Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning - Pre-Trained Policy Discriminators are General Reward Models - RL探究 - UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY - A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF - THE TRICKLE-DOWN IMPACT OF REWARD (IN-)CONSISTENCY ON RLHF - Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback - HUMAN FEEDBACK IS NOT GOLD STANDARD - CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM - Language Models Resist Alignment - Towards a Unified View of Preference Learning for Large Language Models: A Survey ### Memory > 脱离上文长度这个狭窄的视角重新看待模型记忆 - A-MEM: Agentic Memory for LLM Agents - MemInsight: Autonomous Memory Augmentation for LLM Agents - G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems - AGENT WORKFLOW MEMORY - KBLAM: KNOWLEDGE BASE AUGMENTED LANGUAGE MODEL - MIRIX: Multi-Agent Memory System for LLM-Based Agents - M3-Agent: Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory - MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations - Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning - Multiple Memory Systems for Enhancing the Long-term Memory of Agent - PerPilot: Personalizing VLM-based Mobile Agents via Memory and Exploration - Coarse-to-Fine Grounded Memory for LLM Agent Planning - Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory - Memp: Exploring Agent Procedural Memory - RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory - A-MEM: Agentic Memory for LLM Agents - MemoryBank: Enhancing Large Language Models with Long-Term Memory - Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors - Cognitive Architectures for Language Agents - Reason ingBank: Scaling Agent Self-Evolving with Reasoning Memory - LIGHTMEM: LIGHTWEIGHT AND EFFICIENT MEMORY-AUGMENTED GENERATION - Titans: Learning to Memorize at Test Time - Learning to Reason from Feedback at Test-Time - Deep Researcher with Test-Time Diffusion - It’s All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization - Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents - MEMRL: SELF-EVOLVING AGENTS VIA RUNTIME REINFORCEMENT LEARNING ON EPISODIC MEMORY ### 多轮对话 >- 近期我们也陷入多轮对话优化，发现了角色混乱、理解下降等很多问题 - LLMS GET LOST IN MULTI-TURN CONVERSATION ### 指令微调&对齐 (instruction_tunning) - 经典方案 - Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS :star: - Flan-T5: Scaling Instruction-Finetuned Language Models - ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning - Instruct-GPT: Training language models to follow instructions with human feedback :star: - T0: MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION - Natural Instructions: Cross-Task Generalization via Natural Language Crowdsourcing Instructions - Tk-INSTRUCT: SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks - ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-shot Generalization - Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor - INSTRUCTEVAL Towards Holistic Evaluation of Instrucion-Tuned Large Language Models - SFT数据Scaling Law - LIMA: Less Is More for Alignment :star: - Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning - AlpaGasus: Training A Better Alpaca with Fewer Data - InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4 - Instruction Mining: High-Quality Instruction Data Selection for Large Language Models - Visual Instruction Tuning with Polite Flamingo - Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases - Scaling Relationship on Learning Mathematical Reasoning with Large Language Models - WHEN SCALING MEETS LLM FINETUNING: THE EFFECT OF DATA, MODEL AND FINETUNING METHOD - 新对齐/微调方案 - WizardLM: Empowering Large Language Models to Follow Complex Instructions :star: - Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning - Self-Alignment with Instruction Backtranslation :star: - Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models - Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks - PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions - OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs - Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback - Human-like systematic generalization through a meta-learning neural network - Magicoder: Source Code Is All You Need - Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models - Generative Representational Instruction Tuning - InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions - The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions - Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing - 指令数据生成 - APE: LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS :star: - SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions :star: - iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting - Flipped Learning: Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners - Fairness-guided Few-shot Prompting for Large Language Models - Instruction induction: From few examples to natural language task descriptions . - SELF-QA Unsupervised Knowledge Guided alignment. - GPT Self-Supervision for a Better Data Annotator - The Flan Collection Designing Data and Methods - Self-Consuming Generative Models Go MAD - InstructEval: Systematic Evaluation of Instruction Selection Methods - Overwriting Pretrained Bias with Finetuning Data - Improving Text Embeddings with Large Language Models - MAGPIE: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing - Scaling Synthetic Data Creation with 1,000,000,000 Personas - UNLEASHING REASONING CAPABILITY OF LLMS VIA SCALABLE QUESTION SYNTHESIS FROM SCRATCH - A Survey on Data Synthesis and Augmentation for Large Language Models - AgentInstruct: Toward Generative Teaching with Agentic Flows - Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models - 如何降低通用能力损失 - How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition - TWO-STAGE LLM FINE-TUNING WITH LESS SPECIALIZATION AND MORE GENERALIZATION - 微调经验/实验报告 - BELLE: Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases - Baize: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data - A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Large LM - Exploring ChatGPT’s Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences - Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation - Fine tuning LLMs for Enterprise: Practical Guidelines and Recommendations - Others - Crosslingual Generalization through Multitask Finetuning - Cross-Task Generalization via Natural Language Crowdsourcing Instructions - UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models - PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts - ROLELLM: BENCHMARKING, ELICITING, AND ENHANCING ROLE-PLAYING ABILITIES OF LARGE LANGUAGE MODELS ### LLM Agent 让模型使用工具 (llm_agent) - AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION - A Survey on Large Language Model based Autonomous Agents - PERSONAL LLM AGENTS: INSIGHTS AND SURVEY ABOUT THE CAPABILITY, EFFICIENCY AND SECURITY - 基于prompt通用方案 - ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS :star: - Self-ask: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS :star: - MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning - PAL: Program-aided Language Models - ART: Automatic multi-step reasoning and tool-use for large language models - ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models :star: - Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions - Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models :star: - Faithful Chain-of-Thought Reasoning - Reflexion: Language Agents with Verbal Reinforcement Learning :star: - Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework - RestGPT: Connecting Large Language Models with Real-World RESTful APIs - ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models - InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems - TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents - ControlLLM: Augment Language Models with Tools by Searching on Graphs - Reflexion: an autonomous agent with dynamic memory and self-reflection - AutoAgents: A Framework for Automatic Agent Generation - GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension - PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability - TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS :star: -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls - AIOS: LLM Agent Operating System - LLMCompiler An LLM Compiler for Parallel Function Calling - Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval - 基于微调通用方案 - TALM: Tool Augmented Language Models - Toolformer: Language Models Can Teach Themselves to Use Tools :star: - Tool Learning with Foundation Models - Tool Maker：Large Language Models as Tool Maker - TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs - AgentTuning: Enabling Generalized Agent Abilities for LLMs - SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks - FireAct: Toward Language Agent Fine-tuning - Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning - REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT - Efficient Tool Use with Chain-of-Abstraction Reasoning - Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models - AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning - Agent Lumos: Unified and Modular Training for Open-Source Language Agents - ToolGen: Unified Tool Retrieval and Calling via Generation - Scaling Agents via Continual Pre-training - LIMI: Less is More for Agency - 调用模型方案 - HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace - Gorilla：Large Language Model Connected with Massive APIs :star: - OpenAGI: When LLM Meets Domain Experts - 垂直领域 - 数据分析 - DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning - InsightLens: Discovering and Exploring Insights from Conversational Contexts in Large-Language-Model-Powered Data Analysis - Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow - Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System - TaskWeaver: A Code-First Agent Framework - Automated Social Science: Language Models as Scientist and Subjects - Data Interpreter: An LLM Agent For Data Science - FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data - 金融 - WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine - FinGPT: Open-Source Financial Large Language Models - FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design - AlphaFin：使用检索增强股票链框架对财务分析进行基准测试 - FinAgent： A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist :star: - Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in stock Selection - ENHANCING ANOMALY DETECTION IN FINANCIAL MARKETS WITH AN LLM-BASED MULTI-AGENT FRAMEWORK - TRADINGGPT: MULTI-AGENT SYSTEM WITH LAYERED MEMORY AND DISTINCT CHARACTERS FOR ENHANCED FINANCIAL TRADING PERFORMANCE - FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models - LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction - Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment - Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs - TradExpert: Revolutionizing Trading with Mixture of Expert LLMs - FinVision: A Multi-Agent Framework for Stock Market Prediction - AI in Investment Analysis: LLMs for Equity Stock Ratings - AAPM: Large Language Model Agent-based Asset Pricing Models - FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making - TradingAgents: Multi-Agents LLM Financial Trading Framework - Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading - FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents - FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database - FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading - Ploutos: Towards interpretable stock movement prediction with financial large language model - HedgeAgents: A Balanced-aware Multi-agent Financial Trading System - TIMERAG: BOOSTING LLM TIME SERIES FORECASTING VIA RETRIEVAL-AUGMENTED GENERATION - CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction - Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? - Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges - AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions - 生物医疗 - GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information - ChemCrow Augmenting large language models with chemistry tools - Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence - Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents - Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering - CHEMAGENT: SELF-UPDATING LIBRARY IN LARGE LANGUAGE MODELS IMPROVES CHEMICAL REASONING - web/mobile Agent - AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent - A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis - Mind2Web: Towards a Generalist Agent for the Web - MiniWoB++ Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration - WEBARENA: A REALISTIC WEB ENVIRONMENT FORBUILDING AUTONOMOUS AGENTS - AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation - WebLINX: Real-World Website Navigation with Multi-Turn Dialogue - WebVoyager: Building an End-to-end Web Agent with Large Multimodal Models - CogAgent: A Visual Language Model for GUI Agents - Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration - WebCanvas: Benchmarking Web Agents in Online Environments - The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use - UI-TARS: Pioneering Automated GUI Interaction with Native Agents - Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web - WebSailor: Navigating Super-human Reasoning for Web Agent - WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization - WebWatcher: Breaking New Frontiers of Vision-Language Deep Research Agent - OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis - Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents - Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents - Watch and Learn: Learning to Use Computers from Online Videos - Fara-7B: An Efficient Agentic Model for Computer Use - software engineer - Agents in Software Engineering: Survey, Landscape, and Vision - ChatDev: Communicative Agents for Software Development - Research Agent - PaSa: An LLM Agent for Comprehensive Academic Paper Search - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models - Agent Laboratory: Using LLM Agents as Research Assistants - Automated Hypothesis Validation with Agentic Sequential Falsifications - Towards an AI co-scientist - AI4Research: A Survey of Artificial Intelligence for Scientific Research - Kosmos: An AI Scientist for Autonomous Discovery - Knowledge-Informed Automatic Feature Extraction via Collaborative Large Language Model Agents - 设计 - PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs - Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers - 其他 - WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents - ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings - PointLLM: Empowering Large Language Models to Understand Point Clouds - Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models - CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering - SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING - 评估 - Evaluating Verifiability in Generative Search Engines - Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions - API-Bank: A Benchmark for Tool-Augmented LLMs - ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs - Automatic Evaluation of Attribution by Large Language Models - Benchmarking Large Language Models in Retrieval-Augmented Generation - ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems - Agent-as-a-Judge: Evaluate Agents with Agents - MultiAgent - An Empirical Study of Agent Developer Practices in AI Agent Frameworks - GENERATIVE AGENTS - LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS - War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars - Small LLMs Are Weak Tool Learners: A Multi-LLM Agent - Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models - Generative Agents: Interactive Simulacra of Human Behavior :star: - AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents - System-1.x: Learning to Balance Fast and Slow Planning with Language Models - Agents Thinking Fast and Slow:A Talker-Reasoner Architecture - Generative Agent Simulations of 1,000 People - Advanced Reasoning and Learning for Autonomous AI Agents - Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies - Emergent Coordination in Multi-Agent Language Models - TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture - SOLVING A MILLION-STEP LLM TASK WITH ZERO ERRORS - Latent Collaboration in Multi-Agent Systems - 多智能体系统 - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence - MULTI-AGENT COLLABORATION: HARNESSING THE POWER OF INTELLIGENT LLM AGENTS - Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks - Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation - 任务型智能体协作 - METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE - CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society :star: - Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf - Communicative Agents for Software Development :star: - MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning - METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK - 智能体路由 - One Agent To Rule Them All: Towards Multi-agent Conversational AI - A Multi-Agent Conversational Recommender System - 基座模型路由&Ensemble - Large Language Model Routing with Benchmark Datasets - LLM-BL E N D E R: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion - RouteLLM: Learning to Route LLMs with Preference Data - More Agents Is All You Need - Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models - 自主学习和探索进化 - AppAgent: Multimodal Agents as Smartphone Users - Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution - LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error - Empowering Large Language Model Agents through Action Learning - Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents - OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT - LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD - PAST AS A GUIDE: LEVERAGING RETROSPECTIVE LEARNING FOR PYTHON CODE COMPLETION - AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents - A Survey on Self-Evolution of Large Language Models - ExpeL: LLM Agents Are Experiential Learners - ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy - PROACTIVE AGENT: SHIFTING LLM AGENTS FROM REACTIVE RESPONSES TO ACTIVE ASSISTANCE - From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning - AGILE: A Novel Reinforcement Learning Framework of LLM Agents - Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents - ARMAP: SCALING AUTONOMOUS AGENTS VIA AUTOMATIC REWARD MODELING AND PLANNING - Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning - Contextual Experience Replay for Continual Learning of Language Agents - TaskCraft: Automated Generation of Agentic Tasks - MCP - SCALEMCP: DYNAMIC AND AUTO-SYNCHRONIZING MODEL CONTEXT PROTOCOL TOOLS FOR LLM AGENTS - LIVEMCP-101: STRESS TESTING AND DIAGNOSING MCP-ENABLED AGENTS ON CHALLENGING QUERIES - 其他 - LLM+P: Empowering Large Language Models with Optimal Planning Proficiency - Inference with Reference: Lossless Acceleration of Large Language Models - RecallM: An Architecture for Temporal Context Understanding and Question Answering - LLaMA Rider: Spurring Large Language Models to Explore the Open World - LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks - Routine: A Structural Planning Framework for LLM Agent System in Enterprise - Custom Agent - Creating General User Models from Computer Use ### RAG - 经典论文 - WebGPT：Browser-assisted question-answering with human feedback - WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences - WebCPM: Interactive Web Search for Chinese Long-form Question Answering :star: - REPLUG: Retrieval-Augmented Black-Box Language Models :star: - RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit - Atlas: Few-shot Learning with Retrieval Augmented Language Models - RRAML: Reinforced Retrieval Augmented Machine Learning - FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION - 微调 - RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback - RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING - CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS - RAFT: Adapting Language Model to Domain Specific RAG - Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence - 其他论文 - Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation - PDFTriage: Question Answering over Long, Structured Documents - Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading :star: - Active Retrieval Augmented Generation - kNN-LM Does Not Improve Open-ended Text Generation - Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model - DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries - Factuality Enhanced Language Models for Open-Ended Text Generation - KwaiAgents: Generalized Information-seeking Agent System with Large Language Models - Complex Claim Verification with Evidence Retrieved in the Wild - Retrieval-Augmented Generation for Large Language Models: A Survey - ChatQA: Building GPT-4 Level Conversational QA Models - RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture - Benchmarking Large Language Models in Retrieval-Augmented Generation - T-RAG: Lessons from the LLM Trenches - ARAGOG: Advanced RAG Output Grading - ActiveRAG: Revealing the Treasures of Knowledge via Active Learning - OpenResearcher: Unleashing AI for Accelerated Scientific Research - [Contextual.ai-RAG2.0](https://contextual.ai/introducing-rag2/) - Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation - Memory3 : Language Modeling with Explicit Memory - 优化检索 - IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions - HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels - PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES - Query Rewriting for Retrieval-Augmented Large Language Models - Query2doc: Query Expansion with Large Language Models :star: - Query Expansion by Prompting Large Language Models :star: - [Anthropic Contextual Retrieval](https://www.anthropic.com/news/contextual-retrieval) - Multi-Level Querying using A Knowledge Pyramid - A Survey of Query Optimization in Large Language Models - Ranking - A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models - RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models - Improving Passage Retrieval with Zero-Shot Question Generation - Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting - RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs - Ranking Manipulation for Conversational Search Engines - Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents - Opensource Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking - T2Ranking: A large-scale Chinese Benchmark for Passage Ranking - Learning to Filter Context for Retrieval-Augmented Generation - 传统搜索方案 - ASK THE RIGHT QUESTIONS:ACTIVE QUESTION REFORMULATION WITH REINFORCEMENT LEARNING - Query Expansion Techniques for Information Retrieval a Survey - Learning to Rewrite Queries - Managing Diversity in Airbnb Search - 新向量模型用于Recall和Ranking - Augmented Embeddings for Custom Retrievals - BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation - [网易为RAG设计的BCE Embedding技术报告](https://zhuanlan.zhihu.com/p/681370855) - BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models - D2LLM: Decomposed and Distilled Large Language Models for Semantic Search - Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training - UniSearch: Rethinking Search System with a Unified Generative Architecture - UniDex: Rethinking Search Inverted Indexing with Unified Semantic Modeling - 优化推理结果 - Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting - 动态RAG（When to Search & Search Plan） - SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION :star: - Self-Knowledge Guided Retrieval Augmentation for Large Language Models - Self-DC: When to retrieve and When to generate Self Divide-and-Conquer for Compositional Unknown Questions - Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs - Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity - REAPER: Reasoning based Retrieval Planning for Complex RAG Systems - When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively - PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers - ONEGEN: EFFICIENT ONE-PASS UNIFIED GENERATION AND RETRIEVAL FOR LLMS - Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval - Graph RAG - GRAPH Retrieval-Augmented Generation: A Survey - From Local to Global: A Graph RAG Approach to Query-Focused Summarization - GRAG: Graph Retrieval-Augmented Generation - GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning - THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASONING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH - LightRAG: Simple and Fast Retrieval-Augmented Generation - THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASON- ING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH - StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization - Multistep RAG - SYNERGISTIC INTERPLAY BETWEEN SEARCH AND LARGE LANGUAGE MODELS FOR INFORMATION RETRIEVAL - Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions - Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy - RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation - IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues - Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP - Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks - MindSearch 思·索: Mimicking Human Minds Elicits Deep AI Searcher - RQ-RAG: LEARNING TO REFINE QUERIES FOR RETRIEVAL AUGMENTED GENERATION - AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition - Timeline RAG - Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization - fast rag - MINIRAG: TOWARDS EXTREMELY SIMPLE RETRIEVAL-AUGMENTED GENERATION - EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations - Deep Research - Deep Researcher with Test-Time Diffusion ### Other Prompt Engineer(prompt_engineer) - PDL: A Declarative Prompt Programming Language - Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs - Prompting_as_Scientific_Inquiry - Calibrate Before Use: Improving Few-Shot Performance of Language Models - In-Context Instruction Learning - LEARNING PERFORMANCE-IMPROVING CODE EDITS - Boosting Theory-of-Mind Performance in Large Language Models via Prompting - Generated Knowledge Prompting for Commonsense Reasoning - RECITATION-AUGMENTED LANGUAGE MODELS - kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR INFERENCE - EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus - Causality-aware Concept Extraction based on Knowledge-guided Prompting - LARGE LANGUAGE MODELS AS OPTIMIZERS - Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization - Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V - RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions - MedPrompt: Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine - DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines - Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels - In-Context Learning for Extreme Multi-Label Classification - Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs - DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines - CONNECTING LARGE LANGUAGE MODELS WITH EVOLUTIONARY ALGORITHMS YIELDS POWERFUL PROMP OPTIMIZERS - TextGrad: Automatic "Differentiation" via Text - Task Facet Learning: A Structured Approach to Prompt Optimization - LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language - PAS: Data-Efficient Plug-and-Play Prompt Augmentation System - Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models - From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice - Does Prompt Formatting Have Any Impact on LLM Performance? - AUTO-DEMO PROMPTING: LEVERAGING GENERATED OUTPUTS AS DEMONSTRATIONS FOR ENHANCED BATCH PROMPTING - PROMPTBREEDER: SELF-REFERENTIAL SELF-IMPROVEMENT VIA PROMPT EVOLUTION - Psychologically Enhanced AI Agents - Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models - Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics ### 大模型图表理解和生成 - survey - Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study - Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey - Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data - prompt - Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning - Tab-CoT: Zero-shot Tabular Chain of Thought - Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding - fintuning - TableLlama: Towards Open Large Generalist Models for Tables - TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios - multimodal - MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning - ChartLlama: A Multimodal LLM for Chart Understanding and Generation - ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning - ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning - ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning - MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering - UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning - TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning - Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs - TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains - TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy - generative UI - Generative UI: LLMs are Effective UI Generators ### LLM+KG - 综述类 - Unifying Large Language Models and Knowledge Graphs: A Roadmap - Large Language Models and Knowledge Graphs: Opportunities and Challenges - [知识图谱与大模型融合实践研究报告2023](https://blog.csdn.net/m0_37586850/article/details/132463508) - KG用于大模型推理 - Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs - MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models - Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering - Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models - BRING YOUR OWN KG: Self-Supervised Program Synthesis for Zero-Shot KGQA - StructGPT: A General Framework for Large Language Model to Reason over Structured Data - 大模型用于KG构建 - Enhancing Knowledge Graph Construction Using Large Language Models - LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT - ITERATIVE ZERO-SHOT LLM PROMPTING FOR KNOWLEDGE GRAPH CONSTRUCTION - Exploring Large Language Models for Knowledge Graph Completion ### Humanoid Agents - HABITAT 3.0: A CO-HABITAT FOR HUMANS, AVATARS AND ROBOTS - Humanoid Agents: Platform for Simulating Human-like Generative Agents - Voyager: An Open-Ended Embodied Agent with Large Language Models - [Shaping the future of advanced robotics](https://deepmind.google/discover/blog/shaping-the-future-of-advanced-robotics/) - AUTORT: EMBODIED FOUNDATION MODELS FOR LARGE SCALE ORCHESTRATION OF ROBOTIC AGENTS - ROBOTIC TASK GENERALIZATION VIA HINDSIGHT TRAJECTORY SKETCHES - ALFWORLD: ALIGNING TEXT AND EMBODIED ENVIRONMENTS FOR INTERACTIVE LEARNING - MINEDOJO: Building Open-Ended Embodied Agents with Internet-Scale Knowledge - LEGENT: Open Platform for Embodied Agents ### pretrain_data & pretrain - DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining - The Pile: An 800GB Dataset of Diverse Text for Language Modeling - CCNet: Extracting High Quality Monolingual Datasets fromWeb Crawl Data - WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models - CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model - In-Context Pretraining: Language Modeling Beyond Document Boundaries - Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance - Zyda: A 1.3T Dataset for Open Language Modeling - Entropy Law: The Story Behind Data Compression and LLM Performance - Data, Data Everywhere: A Guide for Pretraining Dataset Construction - Data curation via joint example selection further accelerates multimodal learning - IMPROVING PRETRAINING DATA USING PERPLEXITY CORRELATIONS - AI models collapse when trained on recursively generated data ### 领域模型SFT(domain_llms) - 金融 - BloombergGPT： A Large Language Model for Finance - FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis - CFGPT: Chinese Financial Assistant with Large Language Model - CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model - InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning - BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark - PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance - The FinBen: An Holistic Financial Benchmark for Large Language Models - XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters - Towards Trustworthy Large Language Models in Industry Domains - When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments - A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges - 生物医疗 - MedGPT: Medical Concept Prediction from Clinical Narratives - BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining - PubMed GPT: A Domain-specific large language model for biomedical text :star: - ChatDoctor：Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge - Med-PaLM：Large Language Models Encode Clinical Knowledge[V1,V2] :star: - SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support - Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue - 其他 - Galactia：A Large Language Model for Science - Augmented Large Language Models with Parametric Knowledge Guiding - ChatLaw Open-Source Legal Large Language Model :star: - MediaGPT : A Large Language Model For Chinese Media - KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering - EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce - TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT - LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS - MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT - PLLaMa: An Open-source Large Language Model for Plant Science - ADAPTING LARGE LANGUAGE MODELS VIA READING COMPREHENSION ### LLM超长文本处理 (long_input) - 位置编码、注意力机制优化 - Unlimiformer: Long-Range Transformers with Unlimited Length Input - Parallel Context Windows for Large Language Models - [苏剑林, NBCE：使用朴素贝叶斯扩展LLM的Context处理长度](https://spaces.ac.cn/archives/9617) :star: - Structured Prompting: Scaling In-Context Learning to 1,000 Examples - Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens - Scaling Transformer to 1M tokens and beyond with RMT - TRAIN SHORT, TEST LONG: ATTENTION WITH LINEAR BIASES ENABLES INPUT LENGTH EXTRAPOLATION :star: - Extending Context Window of Large Language Models via Positional Interpolation - LongNet: Scaling Transformers to 1,000,000,000 Tokens - https://kaiokendev.github.io/til#extending-context-to-8k - [苏剑林,Transformer升级之路：10、RoPE是一种β进制编码](https://spaces.ac.cn/archives/9675) :star: - [苏剑林,Transformer升级之路：11、将β进制位置进行到底](https://spaces.ac.cn/archives/9706) - [苏剑林,Transformer升级之路：12、无限外推的ReRoPE？](https://spaces.ac.cn/archives/9708) - [苏剑林,Transformer升级之路：15、Key归一化助力长度外推](https://spaces.ac.cn/archives/9859) - EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS - Ring Attention with Blockwise Transformers for Near-Infinite Context - YaRN: Efficient Context Window Extension of Large Language Models - LM-INFINITE: SIMPLE ON-THE-FLY LENGTH GENERALIZATION FOR LARGE LANGUAGE MODELS - EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS - Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention - 上文压缩排序方案 - Lost in the Middle: How Language Models Use Long Contexts :star: - LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models - LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression :star: - Learning to Compress Prompts with Gist Tokens - Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering - LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration - PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models - Are Long-LLMs A Necessity For Long-Context Tasks? - QwenLong-CPRS: Towards \infty-LLMs with Dynamic Context Optimization - 训练和模型架构方案 - Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS - Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon - Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering - Focused Transformer: Contrastive Training for Context Scaling - Effective Long-Context Scaling of Foundation Models - ON THE LONG RANGE ABILITIES OF TRANSFORMERS - Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer - POSE: EFFICIENT CONTEXT WINDOW EXTENSION OF LLMS VIA POSITIONAL SKIP-WISE TRAINING - LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS - LongAlign: A Recipe for Long Context Alignment of Large Language Models - Data Engineering for Scaling Language Models to 128K Context - MEGALODON: Efficient LLM Pretraining and Inference with Unlimited Context Length - Make Your LLM Fully Utilize the Context - Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models - LIFT: Improving Long Context Understanding of Large Language Models through Long Input Fine-Tuning - REFRAG: Rethinking RAG based Decoding - 效率优化 - Efficient Attention: Attention with Linear Complexities - Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention - HyperAttention: Long-context Attention in Near-Linear Time - FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness - With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation - 评估 - NOLIMA: Long-Context Evaluation Beyond Literal Matching - The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs - 原理分析 - Retrieval Head Mechanistically Explains Long-Context Factuality ### LLM长文本生成（long_output） - Re3 : Generating Longer Stories With Recursive Reprompting and Revision - RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text - DOC: Improving Long Story Coherence With Detailed Outline Control - Weaver: Foundation Models for Creative Writing - Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models - Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations - Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models ### NL2SQL - 大模型方案 - DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction :star: - C3: Zero-shot Text-to-SQL with ChatGPT :star: - SQL-PALM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL - BIRD Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQL :star: - A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL - ChatDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY - A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability - Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning - Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios - Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling - Domain Knowledge Intensive - Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge - Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion - Towards Robustness of Text-to-SQL Models against Synonym Substitution - FinQA: A Dataset of Numerical Reasoning over Financial Data - others - RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL - MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL ### Code Generation - Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering - Codeforces as an Educational Platform for Learning Programming in Digitalization - Competition-Level Code Generation with AlphaCode - CODECHAIN: TOWARDS MODULAR CODE GENERATION THROUGH CHAIN OF SELF-REVISIONS WITH REPRESENTATIVE SUB-MODULES - AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation ### 降低模型幻觉 (reliability) - Survey - Large language models and the perils of their hallucinations - Survey of Hallucination in Natural Language Generation - Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models - A Survey of Hallucination in Large Foundation Models - A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions - Calibrated Language Models Must Hallucinate - Why Does ChatGPT Fall Short in Providing Truthful Answers? - Why Language Models Hallucinate - Prompt or Tunning - R-Tuning: Teaching Large Language Models to Refuse Unknown Questions - PROMPTING GPT-3 TO BE RELIABLE - ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS :star: - On the Advance of Making Language Models Better Reasoners - RefGPT: Reference → Truthful & Customized Dialogues Generation by GPTs and for GPTs - Rethinking with Retrieval: Faithful Large Language Model Inference - GENERATE RATHER THAN RETRIEVE: LARGE LANGUAGE MODELS ARE STRONG CONTEXT GENERATORS - Large Language Models Struggle to Learn Long-Tail Knowledge - Decoding Strategy - Trusting Your Evidence: Hallucinate Less with Context-aware Decoding :star: - SELF-REFINE:ITERATIVE REFINEMENT WITH SELF-FEEDBACK :star: - Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference - Inference-Time Intervention: Eliciting Truthful Answers from a Language Model - Enabling Large Language Models to Generate Text with Citations - Factuality Enhanced Language Models for Open-Ended Text Generation - KL-Divergence Guided Temperature Sampling - KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection - CONTRASTIVE DECODING IMPROVES REASONING IN LARGE LANGUAGE MODEL - Contrastive Decoding: Open-ended Text Generation as Optimization - Probing and Detection - Automatic Evaluation of Attribution by Large Language Models - QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization - Zero-Resource Hallucination Prevention for Large Language Models - LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples - Language Models (Mostly) Know What They Know :star: - LM vs LM: Detecting Factual Errors via Cross Examination - Do Language Models Know When They’re Hallucinating References? - SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models - SELF-CONTRADICTORY HALLUCINATIONS OF LLMS: EVALUATION, DETECTION AND MITIGATION - Self-consistency for open-ended generations - Improving Factuality and Reasoning in Language Models through Multiagent Debate - Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models - Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs - Reviewing and Calibration - Truth-o-meter: Collaborating with llm in fighting its hallucinations - RARR: Researching and Revising What Language Models Say, Using Language Models - CRITIC: LARGE LANGUAGE MODELS CAN SELFCORRECT WITH TOOL-INTERACTIVE CRITIQUING - VALIDATING LARGE LANGUAGE MODELS WITH RELM - PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions - Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback - Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes - Woodpecker: Hallucination Correction for Multimodal Large Language Models - Zero-shot Faithful Factual Error Correction - LARGE LANGUAGE MODELS CANNOT SELF-CORRECT REASONING YET - Training Language Models to Self-Correct via Reinforcement Learning - Training LLMs for Honesty via Confessions ### 大模型评估（evaluation） - 事实性评估 - TRUSTWORTHY LLMS: A SURVEY AND GUIDELINE FOR EVALUATING LARGE LANGUAGE MODELS’ ALIGNMENT - TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models - TRUE: Re-evaluating Factual Consistency Evaluation - FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation - KoLA: Carefully Benchmarking World Knowledge of Large Language Models - When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories - FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios - LONG-FORM FACTUALITY IN LARGE LANGUAGE MODELS - 检测任务 - Detecting Pretraining Data from Large Language Models - Scalable Extraction of Training Data from (Production) Language Models - Rethinking Benchmark and Contamination for Language Models with Rephrased Samples - 通用评估 - G-EVAL: NLG Evaluation using GPT-4 with Better Human Alignment - 工具调用评估 - ToolRM: Outcome Reward Models for Tool-Calling Large Language Models - Agent 评估 - SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? - ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering - FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning - Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First ### 推理优化(inference) - Fast Transformer Decoding: One Write-Head is All You Need - Fast Inference from Transformers via Speculative Decoding - GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints - Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding - SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference - BatchPrompt: Accomplish more with less - You Only Cache Once: Decoder-Decoder Architectures for Language Models - XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models - Precise Length Control in Large Language Models - Top-nσ: Not All Logits Are You Need - context cache - Prompt Cache: Modular Attention Reuse for Low-Latency Inference - SGLang: Efficient Execution of Structured Language Model Programs - Efficient Prompt Caching via Embedding Similarity - ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition - Hydragen: High-Throughput LLM Inference with Shared Prefixes - Efficient Memory Management for Large Language Model Serving with PagedAttention ### 模型知识编辑黑科技(model_edit) - ROME：Locating and Editing Factual Associations in GPT - Transformer Feed-Forward Layers Are Key-Value Memories - MEMIT: Mass-Editing Memory in a Transformer - MEND：Fast Model Editing at Scale - Editing Large Language Models: Problems, Methods, and Opportunities - Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch - Automata-based constraints for language model decoding - SGLang: Efficient Execution of Structured Language Model Programs ### 模型合并和剪枝(model_merge) - Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM - DARE Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch - EDITING MODELS WITH TASK ARITHMETIC - TIES-Merging: Resolving Interference When Merging Models - LM-Cocktail: Resilient Tuning of Language Models via Model Merging - SLICEGPT: COMPRESS LARGE LANGUAGE MODELS BY DELETING ROWS AND COLUMNS - Checkpoint Merging via Bayesian Optimization in LLM Pretrainin - Arcee's MergeKit: A Toolkit for Merging Large Language Models ### MOE - Tricks for Training Sparse Translation Models - ST-MoE: Designing Stable and Transferable Sparse Expert Models - Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity - GLaM: Efficient Scaling of Language Models with Mixture-of-Experts - GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding - OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER - DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale - Dense-to-Sparse Gate for Mixture-of-Experts - Efficient Large Scale Language Modeling with Mixtures of Experts ### Multimodal - InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning - BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models - Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models - LLava Visual Instruction Tuning - MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models - BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions - mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality - LVLM eHub: A Comprehensive Evaluation Benchmark for Large VisionLanguage Models - Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities - PaLM-E: An Embodied Multimodal Language Model - TabLLM: Few-shot Classification of Tabular Data with Large Language Models - AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling - [Sora tech report](https://openai.com/research/video-generation-models-as-world-simulators) - Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study - OCR - Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models - Large OCR Model:An Empirical Study of Scaling Law for OCR - ON THE HIDDEN MYSTERY OF OCR IN LARGE MULTIMODAL MODELS - DeepSeek-OCR: Contexts Optical Compression - PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers - Many-Shot In-Context Learning in Multimodal Foundation Models - Adding Conditional Control to Text-to-Image Diffusion Models - Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs - ShowUI: One Vision-Language-Action Model for GUI Visual Agent - Flamingo: a Visual Language Model for Few-Shot Learning - Segment Anything - Monkey : Image Resolution and Text Label Are Important Things for Large Multi-modal Models - Learning Transferable Visual Models From Natural Language Supervision - AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE - InternVL1: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks - Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models - InternVL1.5： How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites - Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond - Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution ### 综述 - A Survey of Large Language Models - Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing :star: - Paradigm Shift in Natural Language Processing - Pre-Trained Models: Past, Present and Future - What Language Model Architecture and Pretraining objects work best for zero shot generalization :star: - Towards Reasoning in Large Language Models: A Survey - Reasoning with Language Model Prompting: A Survey :star: - An Overview on Language Models: Recent Developments and Outlook :star: - A Survey of Large Language Models[6.29更新版] - Unifying Large Language Models and Knowledge Graphs: A Roadmap - Augmented Language Models: a Survey :star: - Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey - Challenges and Applications of Large Language Models - The Rise and Potential of Large Language Model Based Agents: A Survey - Large Language Models for Information Retrieval: A Survey - AI Alignment: A Comprehensive Survey - Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications - Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook - A Survey on Language Models for Code - Model-as-a-Service (MaaS): A Survey ### 大模型能力探究 - In Context Learning - LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY - How does in-context learning work? A framework for understanding the differences from traditional supervised learning - Why can GPT learn in-context? Language Model Secretly Perform Gradient Descent as Meta-Optimizers :star: - Rethinking the Role of Demonstrations What Makes incontext learning work? :star: - Trained Transformers Learn Linear Models In-Context - In-Context Learning Creates Task Vectors - FUNCTION VECTORS IN LARGE LANGUAGE MODELS - Learning without training: The implicit dynamics of in-context learning - LANGUAGE MODELS ARE INJECTIVE AND HENCE INVERTIBLE - 涌现能力 - Sparks of Artificial General Intelligence: Early experiments with GPT-4 - Emerging Ability of Large Language Models :star: - LANGUAGE MODELS REPRESENT SPACE AND TIME - Are Emergent Abilities of Large Language Models a Mirage? - 能力评估 - IS CHATGPT A GENERAL-PURPOSE NATURAL LANGUAGE PROCESSING TASK SOLVER? - Can Large Language Models Infer Causation from Correlation? - Holistic Evaluation of Language Model - Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond - Theory of Mind May Have Spontaneously Emerged in Large Language Models - Beyond The Imitation Game: Quantifying And Extrapolating The Capabilities Of Language Models - Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations - Demystifying GPT Self-Repair for Code Generation - Evidence of Meaning in Language Models Trained on Programs - Can Explanations Be Useful for Calibrating Black Box Models - On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective - Language acquisition: do children and language models follow similar learning stages? - Language is primarily a tool for communication rather than thought - 领域能力 - Capabilities of GPT-4 on Medical Challenge Problems - Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine - Persona Vectors: Monitoring and Controlling Character Traits in Language Models - 可解释性 - Understanding LLM Embeddings for Regression - [When Models Manipulate Manifolds: The Geometry of a Counting Task](https://transformer-circuits.pub/2025/linebreaks/index.html) - Weight-sparse transformers have interpretable circuits ### Prompt Tunning范式 - Tunning Free Prompt - GPT2: Language Models are Unsupervised Multitask Learners - GPT3: Language Models are Few-Shot Learners :star: - LAMA: Language Models as Knowledge Bases? - AutoPrompt: Eliciting Knowledge from Language Models - Fix-Prompt LM Tunning - T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - PET-TC(a): Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference :star: - PET-TC(b): PETSGLUE It’s Not Just Size That Matters Small Language Models are also few-shot learners - GenPET: Few-Shot Text Generation with Natural Language Instructions - LM-BFF: Making Pre-trained Language Models Better Few-shot Learners :star: - ADEPT: Improving and Simplifying Pattern Exploiting Training - Fix-LM Prompt Tunning - Prefix-tuning: Optimizing continuous prompts for generation - Prompt-tunning: The power of scale for parameter-efficient prompt tuning :star: - P-tunning: GPT Understands Too :star: - WARP: Word-level Adversarial ReProgramming - LM + Prompt Tunning - P-tunning v2: Prompt Tuning Can Be Comparable to Fine-tunning Universally Across Scales and Tasks - PTR: Prompt Tuning with Rules for Text Classification - PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains - Fix-LM Adapter Tunning - LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS :star: - LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning - Parameter-Efficient Transfer Learning for NLP - INTRINSIC DIMENSIONALITY EXPLAINS THE EFFECTIVENESS OF LANGUAGE MODEL FINE-TUNING - DoRA: Weight-Decomposed Low-Rank Adaptation - Representation Tuning - ReFT: Representation Finetuning for Language Models ### Timeseries LLM - TimeGPT-1 - Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook - TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS - Large Language Models Are Zero-Shot Time Series Forecasters - TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING - Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing - Lag-Llama: Towards Foundation Models for Time Series Forecasting - PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting ### Quanization - AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration - LLM-QAT: Data-Free Quantization Aware Training for Large Language Models - LLM.int8() 8-bit Matrix Multiplication for Transformers at Scale - SmoothQuant Accurate and Efficient Post-Training Quantization for Large Language Models ### Adversarial Attacking - Curiosity-driven Red-teaming for Large Language Models - Red Teaming Language Models with Language Models - EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH ### 对话模型 - LaMDA: Language Models for Dialog Applications - Sparrow: Improving alignment of dialogue agents via targeted human judgements :star: - BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage - How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation - DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI - Enhancing Chat Language Models by Scaling High-quality Instructional Conversations - DiagGPT: An LLM-based Chatbot with Automatic Topic Management for Task-Oriented Dialogue ### Others - Pretraining on the Test Set Is All You Need 哈哈作者你是懂讽刺文学的 - Learnware: Small Models Do Big - The economic potential of generative AI - A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models - How People Use ChatGPT - [BenchGecko](https://benchgecko.ai/zh/) - 全面的AI模型基准评测平台，覆盖128个评估基准，支持跨供应商价格对比和AI经济追踪。