Summary of The Factuality Of Large Language Models in the Legal Domain, by Rajaa El Hamdani and Thomas Bonald and Fragkiskos Malliaros and Nils Holzenberger and Fabian Suchanek
The Factuality of Large Language Models in the Legal Domain
by Rajaa El Hamdani, Thomas Bonald, Fragkiskos Malliaros, Nils Holzenberger, Fabian Suchanek
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 The paper investigates the reliability of large language models (LLMs) as knowledge bases in the legal domain. To evaluate this, it designs a dataset of diverse factual questions about case law and legislation, then uses this dataset to assess several LLMs under different evaluation methods, including exact, alias, and fuzzy matching. The results show that performance improves significantly with the alias and fuzzy matching methods. Additionally, the paper explores the impact of abstaining from answering when uncertain and providing in-context examples, finding that both strategies enhance precision. Finally, it demonstrates that pre-training LLMs on legal documents can further improve factual precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models (LLMs) are at being knowledge sources for law-related information. It creates a test set of questions about laws and court cases, then uses this to see how different LLMs do when answering these questions in different ways. The results show that the models do better when they’re allowed to be a little flexible with their answers. The paper also explores what happens when the models get to choose whether or not to answer a question, and it finds that this helps them make more accurate responses. Finally, the paper shows that training LLMs on law-related texts can help them become even better at answering factual questions about laws. |
Keywords
* Artificial intelligence * Precision
