Summary of Lawinstruct: a Resource For Studying Language Model Adaptation to the Legal Domain, by Joel Niklaus et al.
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain
by Joel Niklaus, Lucia Zheng, Arya D. McCarthy, Christopher Hahn, Brian M. Rosen, Peter Henderson, Daniel E. Ho, Garrett Honke, Percy Liang, Christopher Manning
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates whether instruction tuning on legal datasets is necessary to develop strong legal reasoning capabilities in language models. The authors aggregate 58 annotated legal datasets, creating LawInstruct, which covers 17 global jurisdictions and 24 languages across diverse tasks like legal QA and summarization of court cases. The team evaluates their models on LegalBench, measuring legal reasoning across five categories, and MMLU, assessing potential drops in general reasoning capabilities. They find that legal-specific instruction tuning improves performance on LegalBench by 15 points or 50% for the base model size, with no performance drops in MMLU. The authors publish LawInstruct as a resource for further study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make language models better at understanding and working with legal information. Right now, these models aren’t very good at this because they haven’t been trained on enough legal texts. The researchers created a big dataset of legal examples called LawInstruct, which will help train the models. They tested the models using two different methods: one that focuses on legal reasoning and another that looks at general language understanding. They found that the models got much better at understanding legal information when they were trained specifically for this task. |
Keywords
* Artificial intelligence * Instruction tuning * Language understanding * Summarization
