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        <title>Better Scores, Worse Grounding: Hidden Regressions after Fine-Tuning in Dialogue Fact Verification</title>
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    <abstract>In dialogue fact verification (DFV), responses often depend on prior turns for correct interpretation, yet systems are still judged mainly by aggregate benchmark scores. We study a hidden grounding regression: aggregate Macro-F1 improves after fine-tuning while previously correct, context-dependent pronoun cases become newly wrong and show stronger premise-side sensitivity than cases that remain correct. On three referent-annotated audit sets constructed from DialFact and FaithDial, we audit six encoder-only verifiers before and after matched source-specific fine-tuning through prediction-transition analysis and the Premise-Preference Score (PPS), a control-adjusted masking diagnostic. Fine-tuning improves Macro-F1 across the six-model/three-evaluation-set panel, yet newly regressed cases show stronger premise-side sensitivity than stable-correct cases under PPS in 17 of 18 evaluated comparisons, indicating that aggregate gains can conceal regressions on a controlled dialogue-grounding audit.</abstract>
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%0 Conference Proceedings
%T Better Scores, Worse Grounding: Hidden Regressions after Fine-Tuning in Dialogue Fact Verification
%A Park, Hyunkyung
%A Zubiaga, Arkaitz
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F park-zubiaga-2026-better
%X In dialogue fact verification (DFV), responses often depend on prior turns for correct interpretation, yet systems are still judged mainly by aggregate benchmark scores. We study a hidden grounding regression: aggregate Macro-F1 improves after fine-tuning while previously correct, context-dependent pronoun cases become newly wrong and show stronger premise-side sensitivity than cases that remain correct. On three referent-annotated audit sets constructed from DialFact and FaithDial, we audit six encoder-only verifiers before and after matched source-specific fine-tuning through prediction-transition analysis and the Premise-Preference Score (PPS), a control-adjusted masking diagnostic. Fine-tuning improves Macro-F1 across the six-model/three-evaluation-set panel, yet newly regressed cases show stronger premise-side sensitivity than stable-correct cases under PPS in 17 of 18 evaluated comparisons, indicating that aggregate gains can conceal regressions on a controlled dialogue-grounding audit.
%U https://aclanthology.org/2026.sigdial-1.51/
%P 720-737

Markdown (Informal)

[Better Scores, Worse Grounding: Hidden Regressions after Fine-Tuning in Dialogue Fact Verification](https://aclanthology.org/2026.sigdial-1.51/) (Park & Zubiaga, SIGDIAL 2026)

• Better Scores, Worse Grounding: Hidden Regressions after Fine-Tuning in Dialogue Fact Verification (Park & Zubiaga, SIGDIAL 2026)

ACL

• Hyunkyung Park and Arkaitz Zubiaga. 2026. Better Scores, Worse Grounding: Hidden Regressions after Fine-Tuning in Dialogue Fact Verification. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 720–737, Atlanta, Georgia, USA. Association for Computational Linguistics.