A learned transformation matrix minimizes CMI in teacher logits to degrade distillation performance while preserving task accuracy.
Improving factuality and reasoning in language models through multiagent debate
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective
A learned transformation matrix minimizes CMI in teacher logits to degrade distillation performance while preserving task accuracy.
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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.