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How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification

Paper recorded by Signals 4 on 2026-09-22 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.CL · 自然语言处理 · first seen 2026-09-23

Abstract

A key factor in deciding whether to trust an automatic prediction is its confidence score, which should be calibrated to match the actual probability of the prediction being correct. Most confidence calibration metrics target binary or multi-class tasks, while multi-label calibration remains largely underexplored. Multi-label classification tasks, such as assigning medical codes to clinical notes

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#15 most recent of 227 cs.CL papers we have recorded · ↑ newer: Behavior is Not Enough: A Mechanism-Based Evaluation of Social Norm Em · ↓ older: Linguistic Features for Interpretable Textual Entailment
Cite this page: How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification: the #15 most recent of 227 cs.CL papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/how-to-estimate-whether-you-have-found-several-needles-in-a-haystack-measuring-c.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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