Equation-to-Behavior Prompting lets large LLMs match cognitive models like Bayesian updating in persuasion games; RL training cuts small-model belief error by 26.5% and improves diverse training outcomes by 2.5-12%.
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STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.
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Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
Equation-to-Behavior Prompting lets large LLMs match cognitive models like Bayesian updating in persuasion games; RL training cuts small-model belief error by 26.5% and improves diverse training outcomes by 2.5-12%.
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Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.