More capable LLMs produce worse distributional forecasts on superlinear growth time series with tail risks of regime change, with the error concentrated in the upper tail; this reverses on conventional threshold metrics.
International Conference on Machine Learning (ICML) , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.
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When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.