A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling
KDnuggets1546 字 (约 7 分钟)
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LLMs commonly suffer from overconfidence, which can be significantly mitigated by post-hoc calibration methods such as temperature scaling, Platt scaling, and isotonic regression, with temperature scaling being the preferred choice due to its simplicity and effectiveness.
入选理由：2024 NAACL调查显示，LLM在事实问答、代码生成和推理任务中的置信度与实际准确率差距可达30%+。
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