A Survey of Query Optimization in Large Language Models (2412.17558v1)

Abstract: \textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of 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.

• The paper consolidates diverse query optimization techniques to mitigate hallucinations in LLM responses.
• It categorizes methods into expansion, decomposition, disambiguation, and abstraction to improve retrieval accuracy.
• Future work is directed towards standardized evaluation benchmarks and feedback models for enhanced query-processing efficiency.

A Survey of Query Optimization in LLMs

The paper "A Survey of Query Optimization in LLMs" by Mingyang Song and Mao Zheng provides a comprehensive analysis of Query Optimization (QO) techniques within the scope of Retrieval-Augmented Generation (RAG) frameworks. The primary objective of this survey is to consolidate existing techniques, highlight their technological foundations, and demonstrate their potential to enhance the versatility and application of LLMs.

Introduction to RAG and the Role of QO

LLMs have shown considerable success across a variety of domains. However, they often encounter issues related to domain-specific tasks and queries that demand up-to-date or specialized knowledge, resulting in phenomena referred to as "hallucinations." RAG is designed to address these shortcomings by integrating external, real-time retrieval components that allow LLMs to access current information. As RAG models become more sophisticated, incorporating multiple components that contribute to overall performance, QO becomes essential in optimizing the retrieval phase to ensure the accuracy and relevance of responses.

Categorization of Query Optimization Techniques

The survey categorizes QO techniques into four primary approaches: Expansion, Decomposition, Disambiguation, and Abstraction, each addressing different aspects of query complexity.

1. Expansion involves enhancing the original query. This can be divided into internal expansion, which focuses on maximizing the information within existing datasets and external expansion, which incorporates new information from external sources to enrich the query. Techniques such as GenRead, which generates contextual documents from queries, and MUGI, which leverages multiple pseudo references, highlight the potential of LLMs in generating comprehensive responses by expanding queries.
2. Decomposition tackles complex queries by breaking them down into more manageable sub-queries. This approach is vital for multi-hop questions requiring sequential information synthesis. Methods like SELF-ASK and Plan-and-Solve illustrate the benefits of decomposing tasks into simpler steps to enhance the problem-solving efficiency of LLMs.
3. Disambiguation addresses queries that might be ambiguous by refining them to better capture user intent. This process involves techniques to enhance the clarity of queries, leading to more precise retrieval. Innovative tools like EchoPrompt focus on rephrasing queries for improved understanding before retrieval steps.
4. Abstraction aims at providing higher-level conceptual solutions to complex problems, reducing potential errors in intermediate reasoning steps. Techniques under this category encourage models to step back, allow for abstraction, and conceptually reason through complex queries. Step-Back is an example of leveraging designed prompts to guide LLMs towards broader reasoning paths.

Challenges and Future Directions

The paper identifies several key challenges, including the development of benchmarks for consistent evaluation of QO techniques in diverse contexts, improving the efficiency and quality of queries, and the integration of process reward models for nuanced feedback during query optimization. The exploration of new benchmarks could provide standardized mechanisms for evaluating QO across various applications, enhancing comparability and progression in the field.

Conclusion and Implications

This survey serves as a critical resource in understanding the landscape of QO techniques and their utility in enhancing RAG models. The categorization and analysis of these techniques aid in identifying gaps and opportunities for future research, particularly with respect to the complexity and dynamic needs of real-world applications. While current methodologies illustrate the rapid advancement and integration of RAG models with LLMs, future work is encouraged to address existing limitations and explore novel methods to optimize query-processing efficiency and accuracy.