eTracer: Towards Traceable Text Generation via Claim-Level Grounding
Bohao Chu, Qianli Wang, Hendrik Damm, Hui Wang, Ula Muhabbek, Elisabeth Livingstone, Christoph M. Friedrich, Norbert Fuhr

TL;DR
eTracer is a framework for traceable text generation that aligns claims with evidence to improve verification and trustworthiness, especially in high-stakes biomedical applications.
Contribution
It introduces claim-level grounding for verifiable responses, enhancing alignment accuracy and enabling precise source tracing and faithfulness quantification.
Findings
Improved grounding quality over conventional methods
Enhanced verification efficiency for users
Effective claim-evidence alignment in biomedical texts
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Biomedical Text Mining and Ontologies · Artificial Intelligence in Healthcare and Education
