用强化学习提升大模型推理准确率与自信表达的匹配度
On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models
- 设计非可欺骗的奖励机制，防止模型为骗分而故意答错
- 实验发现错误奖励设计会导致模型产生自欺性自信
- 奖励策略可作为超参数，按应用需求灵活调整校准效果
本文研究大语言模型（LLM）在强化学习（RL）框架下同时提升推理准确率与自信表达的能力。提出一种双奖励机制：正确时给予正向奖励，错误时给予不同形式的惩罚性奖励。若奖励设计不当，模型可能通过故意答错来获得高自信表达，形成‘自信奖励劫持’现象。为此，提出不可劫持的奖励方案概念，并构建一系列适用于不同场景的方案。实验表明，在未设计防劫持机制的数据集上，选择性奖励劫持确实存在。同时发现最佳校准-准确率平衡依赖于具体数据集和应用场景，建议将奖励方案作为超参数以适配实际需求。代码已公开于 https://anonymous.4open.science/r/rl-confidence-calibration-9ED4/README.md。
原文摘要 · Abstract (English)
In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reasoning accuracy and verbalize its confidence. Our reward scheme uses two functions for rewarding confidence verbalized by the LLM: one when the LLM is correct and a different one when the LLM is incorrect. With a poorly designed reward scheme, the LLM may be incentivized to answer incorrectly so that it can be confident that its answer is indeed incorrect, a phenomenon that we call confidence reward hacking. We propose the concept of non-hackable confidence reward schemes and define a spectrum of such reward schemes for RL confidence calibration training in LLMs. We demonstrate that selective confidence reward hacking can occur in practical datasets with reward schemes that are not designed to be non-hackable. We also demonstrate that the reward scheme with the best calibration to accuracy tradeoff depends on the dataset and the application, and propose using the reward scheme as a hyperparameter to optimize the tradeoffs in accordance to what is important for the application. The code of our experiments is available in https://anonymous.4open.science/r/rl-confidence-calibration-9ED4/README.md.
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