Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability

• URL: http://arxiv.org/abs/2412.15101v1
• Date: Thu, 19 Dec 2024 17:48:23 GMT
• Title: Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
• Authors: Xiangsen Chen, Xuming Hu, Nan Tang,
• Abstract summary: Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks. Existing RAG frameworks, which usually follows the retrieve-then-read paradigm, often struggle with multi-hop QA with temporal information. This paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information.
• Score: 19.722009684115434
• License:
• Abstract: Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incorporate external knowledge and mitigate their inherent knowledge deficiencies. Despite this progress, existing RAG frameworks, which usually follows the retrieve-then-read paradigm, often struggle with multi-hop QA with temporal information since it has difficulty retrieving and synthesizing accurate time-related information. To address the challenge, this paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information. Our approach begins with a review phase, where decomposed sub-queries are dynamically rewritten with temporal information, allowing for subsequent adaptive retrieval and reasoning process. In addition, we implement adaptive retrieval mechanism to minimize unnecessary retrievals, thus reducing the potential for hallucinations. In the subsequent refine phase, the LLM synthesizes the retrieved information from each sub-query along with its internal knowledge to formulate a coherent answer. Extensive experimental results across multiple datasets demonstrate the effectiveness of our proposed framework, highlighting its potential to significantly improve multi-hop QA capabilities in LLMs.

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