# Study pack: Public Law and Legal Theory Research Paper Series (ssrn-4526219)

- SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4526219
- Full text: `papers/ssrn-4526219/paper.txt`
- Summary (EN): `papers/ssrn-4526219/summary.md`
- Summary (ZH): `papers/ssrn-4526219/summary.zh.md`

## Elevator pitch

Professor Yonathan Arbel of the University of Alabama School of Law argues that Large Language Models (LLMs) introduce "Generative Interpretation," a paradigm shift in legal text analysis. This approach enables AI to parse contracts, identify ambiguities, and predict judicial outcomes, offering a potentially cheaper, more accurate, and accessible method than traditional textualism or contextualism. He posits that generative interpretation can resolve long-standing interpretive debates, enhance access to justice, and fundamentally re-equip legal theory for AI's role as an active interpretive agent in contract law.

## Keywords / concepts

contracts; AI; law

## Suggested questions (for RAG / study)

- What is the paper’s main claim and what problem does it solve?
- What method/data does it use (if any), and what are the main results?
- What assumptions are doing the most work?
- What are the limitations or failure modes the author flags?
- How does this connect to the author’s other papers in this corpus?

_Auto-generated study aid. For canonical content, rely on `paper.txt`/`paper.pdf`._