HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering
Abstract: The Retrieval-Augmented Generation (RAG) approach enhances question-answering systems and dialogue generation tasks by integrating information retrieval (IR) technologies with large language models (LLMs). This strategy, which retrieves information from external knowledge bases to bolster the response capabilities of generative models, has achieved certain successes.
However, current RAG methods still face numerous challenges when dealing with multi-hop queries. For instance, some approaches overly rely on iterative retrieval, wasting too many retrieval steps on compound queries. Additionally, using the original complex query for retrieval may fail to capture content relevant to specific sub-queries, resulting in noisy retrieved content. If this noise is not managed, it can lead to the problem of \textit{noise accumulation}.
To address these issues, we introduce
HANRAG, a novel heuristic-based framework designed to efficiently tackle problems of varying complexity. Led by a powerful revelator, it routes queries, decomposes them into sub-queries, and filters noise from retrieved documents. This enhances the system's adaptability and noise resistance, making it highly capable of handling diverse queries.
We compare the proposed framework against other leading industry methods across various benchmarks. The results demonstrate that our framework exhibits superior performance in both single-hop and multi-hop question-answering tasks. We will release the code and benchmark after this paper is accepted.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: Generation, Information Extraction, Language Modeling
Contribution Types: Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models
Languages Studied: english
Submission Number: 3552
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