PPT-Bench measures how LLMs change answers under epistemic, value, authority, and identity pressures at baseline, single-turn, and multi-turn levels, finding separable inconsistency patterns across five models.
Sycophancy in vision-language models: A systematic analysis and an inference-time mitigation framework.arXiv preprint arXiv:2408.11261
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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.
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Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models
PPT-Bench measures how LLMs change answers under epistemic, value, authority, and identity pressures at baseline, single-turn, and multi-turn levels, finding separable inconsistency patterns across five models.
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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.