Evaluator-specific demonstrations with retrospective reasoning improve LLM simulation of individual preference judges by up to 9.9 points over a non-personalized base judge, while interface telemetry often degrades accuracy.
Proceedings of the 41st International Conference on Machine Learning , articleno =
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Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
A hierarchical genetic algorithm induces overthinking in black-box large reasoning models by perturbing logical structure, achieving up to 26.1x longer outputs on the MATH benchmark.
citing papers explorer
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PERSONAJUDGE: Simulating Individual Human Preference Judgments with Evaluator-Specific Demonstration Data
Evaluator-specific demonstrations with retrospective reasoning improve LLM simulation of individual preference judges by up to 9.9 points over a non-personalized base judge, while interface telemetry often degrades accuracy.
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The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
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Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models
A hierarchical genetic algorithm induces overthinking in black-box large reasoning models by perturbing logical structure, achieving up to 26.1x longer outputs on the MATH benchmark.