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Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models
March 28, 2024, 4:48 a.m. | Hai-Long Nguyen, Duc-Minh Nguyen, Tan-Minh Nguyen, Ha-Thanh Nguyen, Thi-Hai-Yen Vuong, Ken Satoh
cs.CL updates on arXiv.org arxiv.org
Abstract: Large language models with billions of parameters, such as GPT-3.5, GPT-4, and LLaMA, are increasingly prevalent. Numerous studies have explored effective prompting techniques to harness the power of these LLMs for various research problems. Retrieval, specifically in the legal data domain, poses a challenging task for the direct application of Prompting techniques due to the large number and substantial length of legal articles. This research focuses on maximizing the potential of prompting by placing it …
abstract arxiv cs.ai cs.cl data document domain gpt gpt-3 gpt-3.5 gpt-4 harness language language models large language large language models legal llama llms parameters power prompting research retrieval studies type
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