Summary of Exploiting Llms’ Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval, by Hai-long Nguyen et al.
Exploiting LLMs’ Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval
by Hai-Long Nguyen, Tan-Minh Nguyen, Duc-Minh Nguyen, Thi-Hai-Yen Vuong, Ha-Thanh Nguyen, Xuan-Hieu Phan
First submitted to arxiv on: 16 Oct 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 paper proposes a novel approach to statutory law retrieval, leveraging large language models (LLMs) for logical reasoning and enhancing traditional deep learning-based methods. By integrating term-based expansion and query reformulation, the proposed system improves retrieval accuracy for real-life scenarios and non-domain-specific vocabulary. The authors demonstrate the effectiveness of their approach on COLIEE 2022 and 2023 datasets, outperforming top results in the competitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses large language models to help find relevant legal information. This is useful because many current methods rely too much on words and meanings, which can be tricky when dealing with real-life scenarios or non-legal language. The approach combines two strategies: first, it uses LLMs to identify important legal terms and facts related to the situation in a query. Then, it expands on these findings by incorporating more information from term-based expansion and query reformulation. This results in improved retrieval accuracy for both lexical and semantic ranking models. |
Keywords
» Artificial intelligence » Deep learning
