Mutual Verification with Large Language Models for Effective Zero-Shot Query Expansion

Core Concepts

A novel zero-shot query expansion framework that leverages the strengths of both generated and retrieved contextual documents to provide high-quality expansion for improved information retrieval performance.

Abstract

The content presents a novel query expansion framework called MILL (Mutual Verification with Large Language Models) that aims to address the limitations of existing retrieval-based and generation-based query expansion methods.
Key highlights:

Retrieval-based methods often fail to accurately capture search intent, particularly with brief or ambiguous queries. Generation-based methods utilizing large language models (LLMs) generally lack corpus-specific knowledge and entail high fine-tuning costs.
MILL proposes a query-query-document (QQD) generation method that leverages LLMs' zero-shot reasoning ability to produce diverse sub-queries and corresponding documents, better capturing the underlying search intent.
MILL then employs a mutual verification process that synergizes the generated and retrieved documents to identify high-quality contextual documents for optimal expansion.
MILL is a fully zero-shot method and extensive experiments on three public benchmark datasets demonstrate its effectiveness over existing retrieval and generation-based methods.

Stats

MILL significantly outperforms existing retrieval and generation-based methods on TREC-DL-2019, TREC-DL-2020, and BEIR datasets across various metrics like NDCG, AP, Recall, and MRR.
For example, on TREC-DL-2019, MILL achieves NDCG@1000 of 73.74, compared to the best baseline of 72.41.
On TREC-DL-2020, MILL achieves NDCG@1000 of 71.23, compared to the best baseline of 70.56.

Quotes

"To address these gaps, we propose a novel zero-shot query expansion framework utilizing LLMs for mutual verification."
"Specifically, we first design a query-query-document generation method, leveraging LLMs' zero-shot reasoning ability to produce diverse sub-queries and corresponding documents."
"Then, a mutual verification process synergizes generated and retrieved documents for optimal expansion."

Key Insights Distilled From

MILL

by Pengyue Jia,... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2310.19056.pdf

Deeper Inquiries

How can the proposed QQD generation method be further improved to capture even more diverse and relevant contextual information?

The QQD generation method can be enhanced by incorporating more sophisticated prompts that guide the Large Language Models (LLMs) to delve deeper into the nuances of the query. One approach could involve providing specific instructions to the LLMs to explore different aspects or dimensions of the query, leading to the generation of a wider range of sub-queries and corresponding documents. Additionally, leveraging domain-specific knowledge or constraints in the prompts can help steer the LLMs towards producing more contextually relevant information. Furthermore, fine-tuning the prompt design based on feedback from the generated results can iteratively improve the diversity and relevance of the contextual information generated by the LLMs.

What are the potential limitations of the mutual verification approach, and how could it be extended to handle noisy or irrelevant documents more effectively?

One potential limitation of the mutual verification approach is the reliance on the quality of the initial set of generated and pseudo-relevance documents. If the generated documents are inaccurate or the pseudo-relevance documents are noisy, the mutual verification process may not effectively filter out irrelevant information. To address this, the mutual verification framework could be extended by incorporating a more robust document ranking mechanism that considers the credibility and relevance of each document. Additionally, introducing a feedback loop where the system learns from previous verification results to adapt and improve the selection criteria can enhance the approach's ability to handle noisy or irrelevant documents more effectively. Implementing a dynamic threshold for document selection based on the confidence level of the verification process can also help mitigate the impact of noisy data on the final expansion results.

Given the success of MILL in information retrieval, how could the core ideas be applied to other domains that rely on query expansion, such as question answering or dialogue systems?

The core ideas of MILL can be adapted and applied to other domains that require query expansion, such as question answering or dialogue systems, by customizing the framework to suit the specific requirements of these domains. For question answering systems, the mutual verification approach can be utilized to enhance the relevance and diversity of candidate answers generated by LLMs, leading to more accurate and comprehensive responses. In dialogue systems, the QQD generation method can be tailored to prompt the LLMs to generate contextually rich responses that cater to the conversational context and user intent. By integrating the mutual verification framework, noisy or irrelevant responses in dialogue systems can be filtered out, improving the overall quality of the interactions. Overall, by adapting the core ideas of MILL to these domains, the systems can benefit from more effective query expansion techniques, resulting in enhanced performance and user satisfaction.