HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented
  Generation for Multi-hop Question Answering
Authors: name: Duolin Sun, name: Dan Yang, name: Yue Shen, name: Yihan Jiao, name: Zhehao Tan, name: Jie Feng, name: Lianzhen Zhong, name: Jian Wang, name: Peng Wei, name: Jinjie Gu

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 the noise is not managed,
it can lead to the problem of noise accumulation. To address these issues, we
introduce HANRAG, a novel heuristic-based framework designed to efficiently
tackle problems of varying complexity. Driven by a powerful revelator, HANRAG
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 obtains superior
performance in both single-hop and multi-hop question-answering tasks.