Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding

2026-08-25

AI Summary

研究通过后果感知评估方法解决语义准确度高但操作可靠性低的问题，以提高安全关键语言理解的可信度。

Abstract

Can language models be trusted in safety- critical operations? In such settings, strong per- formance on semantic metrics does not guaran- tee operational reliability: a misread altitude, a dropped execution condition, or a confused call- sign may score well under standard F1 yet carry sharply asymmetric operational consequences. We study this problem in air traffic control (ATC), where controller-pilot communication demands near-zero error tolerance, and use consequence-aware evaluation to test whether semantic scores misstate operational reliabil- ity. The framework is instantiated in a con- trolled diagnostic ATC benchmark grounded in aviation standards and feedback from 40 air traffic controllers across three countries. Evaluating 8 models, we uncover a system- atic semantic-safety gap: conventional scores give substantially higher performance estimates than consequence-aware evaluation, even for models that appear reliable under standard met- rics. Risk-aware fine-tuning narrows but does not close this gap, showing that consequence- aware evaluation is a necessary complement to standard NLP metrics before any real safety- critical deployment claim

Problem

• safety-critical operations
• semantic accuracy
• operational reliability

Innovation

• consequence-aware evaluation
• safety-critical operations
• semantic-safety gap
• risk-aware fine-tuning