通过分块筛选提升大模型问答准确率
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
- 用大模型逐块评估相关性，过滤无关内容
- 在事实核查任务上准确率显著高于现有方法
- 适合需要高可靠性的问答与推理场景
使用大语言模型（LLMs）的检索增强生成（RAG）系统常因检索到不相关或松散相关的文本而产生错误回答。现有方法多在文档层面操作，难以有效过滤此类内容。我们提出一种基于大模型的分块筛选框架ChunkRAG，通过语义分块将文档拆分为连贯段落，并利用大模型进行相关性评分，对每个文本块进行评估与筛选。在生成前过滤不相关块，显著减少幻觉并提升事实准确性。实验表明，该方法在需要精确信息检索的任务中优于现有RAG模型，大幅提高准确率。该改进增强了RAG系统的可靠性，特别适用于事实核查与多跳推理等应用场景。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related information. Existing methods, which operate at the document level, fail to effectively filter out such content. We propose LLM-driven chunk filtering, ChunkRAG, a framework that enhances RAG systems by evaluating and filtering retrieved information at the chunk level. Our approach employs semantic chunking to divide documents into coherent sections and utilizes LLM-based relevance scoring to assess each chunk's alignment with the user's query. By filtering out less pertinent chunks before the generation phase, we significantly reduce hallucinations and improve factual accuracy. Experiments show that our method outperforms existing RAG models, achieving higher accuracy on tasks requiring precise information retrieval. This advancement enhances the reliability of RAG systems, making them particularly beneficial for applications like fact-checking and multi-hop reasoning.
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