The paper consolidates risks of overreliance on LLMs, identifies gaps in current measurement approaches, and proposes mitigation strategies to keep AI as a human-compatible thought partner.
Multicalibration for confidence scoring in llms
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Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.
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Measuring and mitigating overreliance to build human-compatible AI
The paper consolidates risks of overreliance on LLMs, identifies gaps in current measurement approaches, and proposes mitigation strategies to keep AI as a human-compatible thought partner.
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Multicalibration Boosting: Theory, Convergence, and Transferability
Unifies MCBoost variants and proves convergence to Bregman projection with rates, finite-sample bounds, and covariate-shift transferability.