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About this paper
John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao, Meghana Bhat, Raghav Jain, Aaron Travis Lee, Jonathan H. Choi and 1 more
Research from Stanford, Northwestern, and collaborators assesses large language models' (LLMs) capacity for complex legal reasoning using synthetically generated tax law problems. LLMs, especially GPT-4, demonstrate emergent legal understanding, achieving over 90% accuracy when provided with relevant legal context and advanced prompting techniques.
Key insights
- Legal reasoning capabilities emerge and improve dramatically with each successive generation of general-purpose large language models.
- Providing relevant legal context through retrieval-augmented generation consistently enhances model performance, with 'gold truth' sources yielding the best results.
- Advanced prompting techniques like Chain-of-Thought and few-shot prompting significantly boost the accuracy of capable LLMs, particularly GPT-4.
Summary by alphaXiv.
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