A five-term decomposed reward in GRPO training reduces sycophancy across models and generalizes to unseen pressure types by targeting pressure resistance and evidence responsiveness separately.
Reasoning isn’t enough: Examining truth-bias and sycophancy in LLMs.arXiv preprint arXiv:2506.21561
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Anonymization in multi-agent debate reduces identity bias by equalizing self and peer weights in a Bayesian update model, quantified by the Identity Bias Coefficient.
LLMs show strong user bias in role-tagged contexts that is amplified by preference alignment and can be reduced or controlled through targeted fine-tuning and DPO.
Reasoning models achieve only 2-11% higher accuracy than non-reasoning models when handling queries with false presuppositions, failing to challenge 26-42% of them and remaining sensitive to presupposition strength.
citing papers explorer
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Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition
A five-term decomposed reward in GRPO training reduces sycophancy across models and generalizes to unseen pressure types by targeting pressure resistance and evidence responsiveness separately.
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When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning
Anonymization in multi-agent debate reduces identity bias by equalizing self and peer weights in a Bayesian update model, quantified by the Identity Bias Coefficient.
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User-Assistant Bias in LLMs
LLMs show strong user bias in role-tagged contexts that is amplified by preference alignment and can be reduced or controlled through targeted fine-tuning and DPO.
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Evaluating Reasoning Models for Queries with Presuppositions
Reasoning models achieve only 2-11% higher accuracy than non-reasoning models when handling queries with false presuppositions, failing to challenge 26-42% of them and remaining sensitive to presupposition strength.