Summary of Legalbench-rag: a Benchmark For Retrieval-augmented Generation in the Legal Domain, by Nicholas Pipitone et al.
LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain
by Nicholas Pipitone, Ghita Houir Alami
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces a new benchmark, LegalBench-RAG, to evaluate the retrieval component of Retrieval-Augmented Generation (RAG) systems in the legal domain. The existing benchmarks focus on generative capabilities but neglect the retrieval step. To bridge this gap, the authors emphasize precise retrieval by extracting minimal, highly relevant text segments from legal documents. This approach allows for efficient processing and reduces latency, as LLMs do not need to process large sequences of imprecise chunks. Additionally, accurate results enable LLMs to generate citations for users. The LegalBench-RAG benchmark consists of 6,858 query-answer pairs over a corpus of over 79M characters, entirely human-annotated by legal experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG systems are becoming important in AI-powered legal applications. This paper makes it possible to evaluate how well RAG systems can find relevant text from legal documents. The authors have created a new benchmark called LegalBench-RAG that helps test the retrieval part of these systems. This is different from existing benchmarks that only look at what the system generates. Instead, LegalBench-RAG focuses on getting the most important information out of legal documents quickly and accurately. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
