From Leniency to Suspicion: How an Open Post-Training Recipe Changes Whom LLM Fact-Checkers Believe

Authors: Nouar AlDahoul , Yasir Zaki

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Abstract: LLMs increasingly fact-check online content with the poster's identity in view. However, it remains unclear which post-training changes how much a model's truth judgement depends on the poster's social class, and how such a change happens inside the model. This paper investigates job-based doubt, i.e., judging a claim false more often when the poster has a lower-status job, using fact-checked claims with randomised poster profiles in base and post-trained checkpoints of six model families. Our results reveal that: 1) post-training creates such doubt only with the open Tülu-3 recipe (OLMo-2 32B, Tülu-3 70B), not with Meta's, Alibaba's or Mistral's; the effect is under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; 2) in OLMo-2 32B, the model does not start noticing the poster's job more, but starts interpreting it differently: a lower-status job pushes the base model toward "true" and the post-trained model toward "false"; and 3) this flip is carried by a single residual-stream direction and happens at supervised fine-tuning. The mechanism was confirmed on 400 new claims with pre-specified predictions. None of seven deployed commercial models doubts lower-status posters more. Our work underscores the need to audit speaker bias per training recipe.

Comments:

10 pages, 4 figures, 6 tables. Preprint, under review

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computers and Society (cs.CY)

Cite as:

arXiv:2610.38532 [cs.AI]

(or

arXiv:2610.38532v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.38532

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arXiv-issued DOI via DataCite

Submission history

From: Yasir Zaki [ view email ]

[v1]

Wed, 28 Oct 2026 09:39:56 UTC (60 KB)

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