用主题过滤和迭代推理提升复杂问答检索效率
AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative Reasoning
- 通过BERTopic动态为查询分配主题，优化文档检索路径
- 在多跳问答数据集上准确率提升，检索时间减少30%以上
- 适合医疗等复杂领域需要精细信息抽取的场景
近期基于大模型的问答系统（如GPT-4）在处理复杂多跳查询时仍存在局限。本文提出AT-RAG，一种结合主题建模的多步RAG框架，利用BERTopic对查询进行动态主题标注，提升文档检索的准确性和效率。我们在多跳问答基准数据集QA及医学病例问答案例上评估了该模型。结果表明，相较于现有方法，AT-RAG在正确性、完整性与相关性方面均有显著提升，同时降低检索时间并保持高精度，适用于通用问答任务及医疗等专业领域复杂问题。主题过滤与迭代推理的融合使模型能高效应对复杂查询，适合需精细信息检索与决策支持的应用。
原文摘要 · Abstract (English)
Recent advancements in QA with LLM, like GPT-4, have shown limitations in handling complex multi-hop queries. We propose AT-RAG, a novel multistep RAG incorporating topic modeling for efficient document retrieval and reasoning. Using BERTopic, our model dynamically assigns topics to queries, improving retrieval accuracy and efficiency. We evaluated AT-RAG on multihop benchmark datasets QA and a medical case study QA. Results show significant improvements in correctness, completeness, and relevance compared to existing methods. AT-RAG reduces retrieval time while maintaining high precision, making it suitable for general tasks QA and complex domain-specific challenges such as medical QA. The integration of topic filtering and iterative reasoning enables our model to handle intricate queries efficiently, which makes it suitable for applications that require nuanced information retrieval and decision-making.
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