eTracer: Polarity-Aware Evidence Grounding
TL;DR
This work formalizes polarity-aware evidence grounding: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label, and proposes eTracer, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response.
Abstract
Evidence grounding makes generated content verifiable by linking statements to their sources. However, effective verifiability requires two properties that existing methods fail to jointly provide: verification granularity, the ability to localize fine-grained evidence for each statement, and verification completeness, the ability to capture both support and contradiction. Existing methods either operate at granularities too coarse for rapid inspection or are support-only. We formalize polarity-aware evidence grounding: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label. We construct eTracer-Bench, a human-annotated benchmark with 7,905 supporting and contradicting sentence pairs across seven domains, covering both explicit and implicit contradictions. We further propose eTracer, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response, with localization and polarity emerging jointly from one mechanism. eTracer achieves the best overall Citation F1 of 82.39, driven by a Contradict F1 of 89.20. A pre-registered within-subjects user study (n = 30) shows that polarity-aware grounding nearly doubles contradiction detection over the document-level baseline (27.9% to 51.9%, Cohen's d = 0.82, p = 0.0003), and suggests a false-completeness bias under which support-only grounding fails to improve over this baseline (23.4% vs. 27.9%). Dataset and code: https://github.com/chubohao/eTracer.
