让检索更可信：用对比学习让模型学会区分事实与误导信息
Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
- 通过对比学习优化嵌入，自动筛选难例强化事实证据
- 在临床试验数据上提升检索准确率，减少幻觉
- 生成逐词归因解释，适合医疗等高安全场景
本文提出自解释对比证据重排序（CER），一种以事实证据为核心的新型检索方法。通过对比学习微调嵌入，并为每个检索段落生成粒度到词的归因理由。利用主观性标准自动选取难负例，迫使模型将事实性解释拉近，而将主观或误导性解释推开。实验结果表明，该方法在临床试验报告上的检索准确率得到提升，有效缓解了RAG系统中的幻觉问题，提供了透明、基于证据的检索结果，显著增强安全性关键领域的可靠性。
原文摘要 · Abstract (English)
This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embeddings with contrastive learning and generating token-level attribution rationales for each retrieved passage. Hard negatives are automatically selected using a subjectivity-based criterion, forcing the model to pull factual rationales closer while pushing subjective or misleading explanations apart. As a result, the method creates an embedding space explicitly aligned with evidential reasoning. We evaluated our method on clinical trial reports, and initial experimental results show that CER improves retrieval accuracy, mitigates the potential for hallucinations in RAG systems, and provides transparent, evidence-based retrieval that enhances reliability, especially in safety-critical domains.
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