混合检索方法显著提升多跳问答准确率，兼顾效率与可解释性。
A Comprehensive Evaluation of Transformer-Based Question Answering Models and RAG-Enhanced Design
- 融合稠密向量与词项重叠的混合检索策略
- 在HotpotQA上准确率提升50%，F1提升47%
- 适合追求高精度且需快速推理的问答系统
基于Transformer的问答模型虽有进展，但多跳推理（需整合多段证据）仍具挑战。本文在检索增强生成框架下，全面评估了多种检索策略：余弦相似度、最大边际相关性，以及融合稠密向量与词项重叠的混合方法。为优化检索，改进EfficientRAG管道，引入标记与迭代精炼，在保持高效的同时提升效果。在HotpotQA数据集上的实验表明，混合方法相比余弦相似度，精确匹配率提升50%，F1得分提升47%。错误分析显示其提升了实体召回与证据互补性，但在处理干扰项和时间推理方面仍有局限。结果表明，混合检索增强生成可作为多跳问答的实用零样本方案，兼顾准确性、效率与可解释性。
原文摘要 · Abstract (English)
Transformer-based models have advanced the field of question answering, but multi-hop reasoning, where answers require combining evidence across multiple passages, remains difficult. This paper presents a comprehensive evaluation of retrieval strategies for multi-hop question answering within a retrieval-augmented generation framework. We compare cosine similarity, maximal marginal relevance, and a hybrid method that integrates dense embeddings with lexical overlap and re-ranking. To further improve retrieval, we adapt the EfficientRAG pipeline for query optimization, introducing token labeling and iterative refinement while maintaining efficiency. Experiments on the HotpotQA dataset show that the hybrid approach substantially outperforms baseline methods, achieving a relative improvement of 50 percent in exact match and 47 percent in F1 score compared to cosine similarity. Error analysis reveals that hybrid retrieval improves entity recall and evidence complementarity, while remaining limited in handling distractors and temporal reasoning. Overall, the results suggest that hybrid retrieval-augmented generation provides a practical zero-shot solution for multi-hop question answering, balancing accuracy, efficiency, and interpretability.
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