From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loop Evaluation with Direct and LLM-Assisted Auditing
Paper recorded by Signals 4 on 2026-10-26 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27
Category: cs.CL · 自然语言处理 · first seen 2026-10-27
Abstract
For large language models (LLMs) and multimodal large language models (MLLMs), model-reported self-confidence is often miscalibrated, yet it may still be useful for efficient evaluation. At the same time, LLM-as-a-judge offers a scalable alternative to direct human auditing, but its practical benefit depends on calibration quality and inference cost.
This paper develops a unified statistical fra
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Cite this page: From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loop Evaluation with Direct and LLM-Assisted Auditing: the #140 most recent of 660 cs.CL papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-self-confidence-to-statistical-accuracy-scaling-human-in-the-loop-evaluatio.html
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