Identification and Honest Recovery from Semantic Observation Kernels: Operator Error, Coarsening, and Stability
Read the original on arXiv Statistics ML →The paper addresses the challenge of extracting reliable posterior uncertainty from probabilistic text generators, such as large language models, which provide phrase-level probabilities that are prompt-dependent and incomplete. It formulates the recovery of the target posterior as a semiparametric inverse problem and introduces honest recovery guarantees that jointly consider calibration error, measurement noise, incomplete probabilities, and weak identification. Simulations and studies on frozen language models validate the method’s coverage and stability, showing how to determine when a semantic measurement can be trusted for inference or when recalibration or abstention is needed.
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