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Estimating and Correcting Yes-No Bias in Language Models
Published in CogSci, 2025
When presented with a yes-no question, humans tend to say “yes” regardless of the ground truth. This “yes-bias” can be attributed either to the social pressure to agree with an interlocutor or simply to the tendency to mimic the distribution of the input data. Here, we estimate “yes-no” response bias in language models (LMs), with the goal of distinguishing the two theories, and explore two strategies for bias correction. We develop two yes-no question datasets derived from existing world knowledge datasets, and test 16 open-weight LMs. We find that LMs often show response bias on yes-no questions, but that it is highly variable, deviating from bias observed in humans. We further present a novel bias correction method, which eliminates bias and improves model performance. Evidence of non-humanlike response bias in LMs informs us on the source of yes-bias in humans, and the efficacy of our bias correction method holds promise for LM evaluation.
Recommended citation: Bhatt, O., & Ivanova, A. (2025). Estimating and Correcting Yes-No Bias in Language Models. Proceedings of the Annual Meeting of the Cognitive Science Society, 47. Retrieved from https://escholarship.org/uc/item/2c04k26b
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RBCorr: Response Bias Correction in Language Models
Published in GEM Workshop @ ACL, 2026
Language models (LMs) are known to be prone to response biases, which present as option preference biases in fixed-response questions. It is therefore imperative to develop low-cost and effective response bias correction methods to improve LM performance and enable more accurate evaluations of model abilities. Here, we propose a simple response bias correction strategy, RBCorr, and test it on 12 open-weight language models using yes-no, entailment, and multiple choice questions. We show that response bias is prevalent in LMs pre-correction and that RBCorr effectively eliminates bias and boosts model performance. We also explore the generalizability of bias behavior across models, datasets, and prompt formats, showing that LogProbs-based correction is highly dependent on all three of these aspects. Overall, RBCorr is an easy-to-use method that can boost the performance of smaller LMs and ensure that LM performance on closed-response benchmarks aligns more closely with their true capabilities.
Recommended citation: Om Bhatt and Anna A Ivanova. 2026. RBCorr: Response Bias Correction in Language Models. In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 540–553, San Diego, California, USA. Association for Computational Linguistics.
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