A Survey of Query Optimization in Large Language Models

• URL: http://arxiv.org/abs/2412.17558v1
• Date: Mon, 23 Dec 2024 13:26:04 GMT
• Title: A Survey of Query Optimization in Large Language Models
• Authors: Mingyang Song, Mao Zheng,
• Abstract summary: RAG mitigates the limitations of Large Language Models by dynamically retrieving and leveraging up-to-date relevant information. QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage.
• Score: 10.255235456427037
• License:
• Abstract: \textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of Large Language Models (LLMs) in understanding and answering queries, especially complex ones in scenarios like Retrieval-Augmented Generation (RAG). Specifically, RAG mitigates the limitations of LLMs by dynamically retrieving and leveraging up-to-date relevant information, which provides a cost-effective solution to the challenge of LLMs producing plausible but potentially inaccurate responses. Recently, as RAG evolves and incorporates multiple components that influence its performance, QO has emerged as a critical element, playing a pivotal role in determining the effectiveness of RAG's retrieval stage in accurately sourcing the necessary multiple pieces of evidence to answer queries correctly. In this paper, we trace the evolution of QO techniques by summarizing and analyzing significant studies. Through an organized framework and categorization, we aim to consolidate existing QO techniques in RAG, elucidate their technological foundations, and highlight their potential to enhance the versatility and applications of LLMs.

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