HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented
  Generation for Multi-hop Question Answering
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.
68c78f3cee0eed1697d6b74b
Duolin Sun
68c78f3cee0eed1697d6b74c
Dan Yang
claimed_verified
2025-09-16T09:43:14.588Z
63f87b14b0ae1748524a8f50
/avatars/e6543d75d115bd34edbd80f322457b75.svg
dan
user
prayerdan
68c78f3cee0eed1697d6b74d
Yue Shen
68c78f3cee0eed1697d6b74e
Yihan Jiao
68c78f3cee0eed1697d6b74f
Zhehao Tan
68c78f3cee0eed1697d6b750
Jie Feng
68c78f3cee0eed1697d6b751
Lianzhen Zhong
68c78f3cee0eed1697d6b752
Jian Wang
68c78f3cee0eed1697d6b753
Peng Wei
68c78f3cee0eed1697d6b754
Jinjie Gu
HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented
  Generation for Multi-hop Question Answering
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.
HANRAG, a heuristic-based framework, improves question-answering systems by efficiently handling multi-hop queries and reducing noise through query decomposition and filtering.
Retrieval-Augmented Generation (RAG) information retrieval (IR) large language models (LLMs) multi-hop queries iterative retrieval sub-queries noise accumulation heuristic-based framework revelator query decomposition noise filtering