Summary of Enhancing Legal Document Retrieval: a Multi-phase Approach with Large Language Models, by Hai-long Nguyen et al.
Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models
by Hai-Long Nguyen, Duc-Minh Nguyen, Tan-Minh Nguyen, Ha-Thanh Nguyen, Thi-Hai-Yen Vuong, Ken Satoh
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract describes a study that explores effective ways to use large language models (LLMs) like GPT-3.5 and GPT-4 for legal text retrieval. The researchers focus on maximizing the potential of LLMs by using them as part of a three-phase system, including BM25 pre-ranking, BERT-based re-ranking, and prompting techniques. They test their approach on the COLIEE 2023 dataset and find that it improves retrieval accuracy. However, they also identify existing issues in the system that need to be addressed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at understanding human language. Researchers have been trying to figure out how to make these models work well for searching through lots of text. One tricky problem is dealing with legal documents, which can be very long and complex. This study tries a new way of using large language models to improve search results. They use three steps: first, they rank the documents based on their relevance (BM25), then they refine that ranking using another method called BERT (BERT-based re-ranking). Finally, they use a technique called prompting to help the model make even better decisions. The researchers tested this approach with real legal texts and found it works much better than just using one step alone. |
Keywords
» Artificial intelligence » Bert » Gpt » Prompting
