DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference
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Computer Science > Computation and Language
arXiv:2601.10896 (cs)
[Submitted on 15 Jan 2026 (v1), last revised 4 Jun 2026 (this version, v2)]
Title:DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference
Authors:Parisa Rabbani, Priyam Sahoo, Ruben Mathew, Aishee Mondal, Harshita Ketharaman, Nimet Beyza Bozdag, Dilek Hakkani-Tür
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Abstract:LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content receives different verdicts when presented as a statement to verify ("Is this statement correct?") versus attributed to a speaker ("Is this speaker correct?"). We call this dialogic deference and introduce DialDefer, a framework for detecting and mitigating these framing-induced judgment shifts. Our Dialogic Deference Score (DDS) captures directional shifts that aggregate accuracy obscures. Across ten domains, 3k+ instances, and five models, conversational framing induces large shifts (mean|DDS|=15.9 percentage points (pp) across models, p < .0001) while accuracy remains stable (<2 pp), with effects amplifying 2--5x on naturalistic Reddit conversations. This effect is domain-dependent: a single model can shift toward disagreement (skepticism) on graduate-level science and toward agreement (deference) on social judgment. Ablations reveal that human-vs-LLM attribution drives the largest shifts (17.7 pp swing), suggesting models treat disagreement with humans as more costly than with AI. Mitigation attempts can reduce deference but over-correct into skepticism, revealing a calibration problem beyond accuracy optimization.
| Comments: | 10 pages main content, 7 figures, 35 pages total with appendix |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.10896 [cs.CL] |
| (or arXiv:2601.10896v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.10896
arXiv-issued DOI via DataCite
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Submission history
From: Parisa Rabbani [view email][v1] Thu, 15 Jan 2026 22:50:46 UTC (10,172 KB)
[v2] Thu, 4 Jun 2026 19:23:12 UTC (9,692 KB)
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View a PDF of the paper titled DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference, by Parisa Rabbani and 6 other authors
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