让生成文本可追溯，每句话都能找到依据。
eTracer: Towards Traceable Text Generation via Claim-Level Grounding
- 逐条比对生成内容与上下文证据，实现精准溯源。
- 相比传统方法，整体溯源准确率显著提升。
- 适合医疗等高风险领域，提升生成内容可信度。
如何高效验证系统生成的回答，特别是在高风险的生物医学领域？为应对这一挑战，我们提出eTracer，一个即插即用的可追溯文本生成框架，通过将每个声明与上下文证据进行关联，实现声明级溯源。该方法在事后阶段将每条生成声明与支持或反驳其的上下文证据对齐。基于声明级溯源结果，eTracer不仅使用户能精确追溯回答来源，还能量化生成内容的忠实度，从而提升生成内容的可验证性与可信度。实验表明，该声明级溯源方法克服了传统句级证据对齐方法的局限，显著提升了整体溯源质量与用户验证效率。代码与数据已公开于https://github.com/chubohao/eTracer。
原文摘要 · Abstract (English)
How can system-generated responses be efficiently verified, especially in the high-stakes biomedical domain? To address this challenge, we introduce eTracer, a plug-and-play framework that enables traceable text generation by grounding claims against contextual evidence. Through post-hoc grounding, each response claim is aligned with contextual evidence that either supports or contradicts it. Building on claim-level grounding results, eTracer not only enables users to precisely trace responses back to their contextual source but also quantifies response faithfulness, thereby enabling the verifiability and trustworthiness of generated responses. Experiments show that our claim-level grounding approach alleviates the limitations of conventional grounding methods in aligning generated statements with contextual sentence-level evidence, resulting in substantial improvements in overall grounding quality and user verification efficiency. The code and data are available at https://github.com/chubohao/eTracer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。