# Speaker bias auditing in LLM fact-checking

On fact-checking claims with randomized poster profiles, the open Tülu-3 post-training recipe makes OLMo-2 32B and Tülu-3 70B judge claims from lower-status posters false more often, an effect under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; Meta's, Alibaba's and Mistral's recipes do not produce it. In OLMo-2 32B the model does not notice the job more but interprets it differently, with a lower-status job pushing the base model toward true and the post-trained model toward false, a flip carried by a single residual-stream direction at supervised fine-tuning. None of seven deployed commercial models doubts lower-status posters more.

On fact-checking claims with randomized poster profiles, the open Tülu-3 post-training recipe makes OLMo-2 32B and Tülu-3 70B judge claims from lower-status posters false more often, an effect under one percentage point but statistically reliable, exceeding training drift and replicating on independent claim sets; Meta's, Alibaba's and Mistral's recipes do not produce it. In OLMo-2 32B the model does not notice the job more but interprets it differently, with a lower-status job pushing the base model toward true and the post-trained model toward false, a flip carried by a single residual-stream direction at supervised fine-tuning. None of seven deployed commercial models doubts lower-status posters more.