Enhancing Lexis AI Search through LLM-Based Query Understanding: Leveraging LLM for Query Reformulation, Expansion, and Extraction

Posted: 5 Feb 2024 Last revised: 28 Feb 2024

Date Written: September 21, 2023

Abstract

In the legal search engine, effectively understanding and processing user queries is crucial for retrieving highly relevant documents. This presentation will explore the application of LLM to improve query understanding, focusing on techniques such as query reformulation, expansion, and extraction. By leveraging LLM, we aim to enhance the search experience and ensure the delivery of pertinent legal information to users. The discussion will cover the underlying methodologies, practical use cases, and actual result. Join us as we explore how state-of-the-art query understanding can transform primary law search and increase the productivity of legal professionals.

Keywords: Query Reformulation, Query Expansion, Query Extraction

Suggested Citation

Kong, Rui and Zhang, Libing and Wang, Samuel, Enhancing Lexis AI Search through LLM-Based Query Understanding: Leveraging LLM for Query Reformulation, Expansion, and Extraction (September 21, 2023). Proceedings of the 7th Annual RELX Search Summit, Available at SSRN: https://ssrn.com/abstract=4716500

Libing Zhang

LexisNexis ( email )

Samuel Wang

LexisNexis ( email )

P. O. Box 933
Dayton, OH 45401
United States

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