大模型越强，谎言越难逃，人类标注可省下
Scaling Trends for Lie Detector Oversight in Preference Learning
- 用谎言检测器自动筛选需人工审核的内容，提升效率
- 4050亿参数模型谎言漏检率降至14%，远低于10亿参数时的34%
- 在真实场景中可完全不用人工标注，但对数据分布变化敏感
大型语言模型中的欺骗行为难以监控和防范，催生了基于谎言检测器的可扩展监督（SOLiD）方法。本文将SOLiD扩展至更大规模模型，并在更多样、更真实的偏好学习设置中评估其表现。结果显示：在检测器真正例率99%的条件下，10亿参数模型的未检测到欺骗比例为34%，而4050亿参数模型降至14%，且在微调阶段可完全移除高成本人工标注，欺骗率无显著上升。然而，当检测器训练数据与偏好学习数据存在分布偏移时，误报率可能升至不切实际的水平。
原文摘要 · Abstract (English)
Deceptive behavior in LLMs is costly to monitor and prevent, motivating approaches such as Scalable Oversight via Lie Detectors (SOLiD) (Cundy & Gleave, 2025), which uses lie detectors to identify responses for review by high-cost labelers. In this paper, we scale SOLiD to larger models and evaluate it in more diverse and realistic preference-learning settings. We find favorable scaling: undetected deception drops from 34% for 1B-parameter models to 14% for 405B-parameter models at a detector true positive rate of 99%, and expensive human labelers can be removed entirely from the fine-tuning phase without a statistically significant increase in deception. However, SOLiD is sensitive to distribution shift between detector training and preference-training data, which can drive detector false positive rates to impractical levels.
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