Flattening Calibration Curves in LLMs: The Vanishing Confidence Signal
Post-training processes for Large Language Models (LLMs) can bias their behavior when encountering content violating safety guidelines. This article, using OpenAI's GPT-4 as an example, explores the failure of model calibration post-training, leading to overconfidence even when wrong. This causes significant false positives in content moderation systems, increasing human review workload. The authors found that upgrading from GPT-4o to GPT-4.1-mini resulted in a vanishing confidence signal, with attempts to recover it failing. This is likely due to information loss during model distillation. To address this, they implemented alternative safeguards like requiring detailed policy explanations and citations, and filtering systems to catch spurious outputs. The article highlights that model upgrades aren't just performance boosts; they cause distributional shifts requiring engineers to re-expose model uncertainty, mitigating associated risks.
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