Leveraging Large Language Models to Enhance Conversational Passage Retrieval

Core Concepts

By leveraging the knowledge and reasoning capabilities of large language models (LLMs), we can generate multiple effective queries to enhance the retrieval performance for complex conversational information seeking tasks.

Abstract

The content discusses methods for improving conversational passage retrieval by leveraging large language models (LLMs). The key points are:

Existing approaches in conversational information seeking (CIS) often model the user's information need with a single rewritten query, which can be limiting for complex queries that require reasoning over multiple facts.

The authors propose a "generate-then-retrieve" (GR) pipeline that first prompts the LLM to generate an answer to the user's query, and then uses that answer to generate multiple searchable queries.

Three GR-based approaches are proposed:

AD: Using the LLM-generated answer as a single long query.
QD: Prompting the LLM to directly generate multiple queries.
AQD: Generating an answer first, then using that to generate multiple queries.
AQDA: A variant of AQD that re-ranks the final results based on the generated answer.

Experiments on the TREC iKAT dataset show that the GR-based approaches, especially AQDA, significantly outperform the traditional "retrieve-then-generate" (RG) baselines.

The authors also address the issue of limited relevance judgments in the official iKAT dataset by creating a new assessment pool using GPT-3.5, which shows high agreement with human labels.

Stats

Travel distance between NYU and Trento
Travel distance between Columbia University and Trento
Travel distance between Rutgers University and Trento

Quotes

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Key Insights Distilled From

Generate then Retrieve

by Zahra Abbasi... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19302.pdf

Deeper Inquiries

How can the proposed methods be extended to generate the final answer to the user's query, in addition to improving the retrieval performance?

The proposed methods can be extended to generate the final answer by incorporating the generated queries into a response generation model. After retrieving relevant passages based on the multiple queries generated by the LLM, the system can analyze the information in these passages to synthesize a coherent and accurate response to the user's query. By leveraging the power of LLMs not only for query generation but also for response generation, the system can provide more comprehensive and contextually relevant answers to the user's questions. This extension would involve integrating a response generation component into the existing pipeline, where the retrieved information is used to construct a final answer that addresses the user's query effectively.

What are the potential biases in the LLM-generated queries, and how can they be mitigated to ensure fair and unbiased retrieval results?

Potential biases in LLM-generated queries can arise from various sources, including the training data, model architecture, and prompt design. Biases may manifest in the form of skewed representations of certain topics, over-reliance on specific types of information, or the propagation of stereotypes present in the training data. To mitigate these biases and ensure fair and unbiased retrieval results, several strategies can be employed:

Diverse Training Data: Ensuring that the LLM is trained on a diverse and representative dataset can help reduce biases in query generation.

Bias Detection: Implementing bias detection mechanisms to identify and flag biased queries generated by the LLM can help in addressing and correcting these biases.

Prompt Design: Crafting prompts that encourage the LLM to generate queries that cover a wide range of perspectives and information sources can help mitigate biases.

Human Oversight: Incorporating human oversight and review of the generated queries can provide an additional layer of quality control to catch and correct any biased queries.

By implementing these strategies and continuously monitoring and refining the query generation process, the biases in LLM-generated queries can be minimized, leading to more fair and unbiased retrieval results.

How can the number of generated queries be optimized to balance retrieval performance and computational efficiency?

Optimizing the number of generated queries is crucial to balance retrieval performance and computational efficiency. Here are some strategies to achieve this balance:

Dynamic Query Generation: Implement a dynamic query generation mechanism that adapts the number of queries based on the complexity of the user query and the available information in the passage collection. For complex queries, more queries can be generated to cover different aspects, while simpler queries may require fewer generated queries.

Threshold-based Approach: Set a threshold for the number of queries based on the information content in the user query. If the query is information-rich and requires detailed retrieval, more queries can be generated, whereas simpler queries may only need a few.

Performance Monitoring: Continuously monitor the retrieval performance based on the number of generated queries. Conduct experiments to determine the optimal number of queries that maximizes performance without sacrificing computational efficiency.

Parallel Processing: Implement parallel processing techniques to generate multiple queries simultaneously, reducing the computational overhead associated with generating multiple queries.

By employing these strategies and fine-tuning the number of generated queries based on the specific requirements of the retrieval task, a balance between retrieval performance and computational efficiency can be achieved.