Taming Overconfidence in LLMs: Reward Calibration in RLHF
Authors: name: Jixuan Leng, name: Chengsong Huang, name: Banghua Zhu, name: Jiaxin Huang

Language model calibration refers to the alignment between the confidence of
the model and the actual performance of its responses. While previous studies
point out the overconfidence phenomenon in Large Language Models (LLMs) and
show that LLMs trained with Reinforcement Learning from Human Feedback (RLHF)
are overconfident with a more sharpened output probability, in this study, we
reveal that RLHF tends to lead models to express verbalized overconfidence in
their own responses. We investigate the underlying cause of this overconfidence
and demonstrate that reward models used for Proximal Policy Optimization (PPO)
exhibit inherent biases towards high-confidence scores regardless of the actual
quality of responses. Building upon this insight, we propose two PPO variants:
PPO-M: PPO with Calibrated Reward Modeling and PPO-C: PPO with Calibrated
Reward Calculation. PPO-M integrates explicit confidence scores in reward model
training, which calibrates reward models to better capture the alignment
between response quality and verbalized confidence. PPO-C adjusts the reward
score during PPO based on the difference between the current reward and the
moving average of past rewards. Both PPO-M and PPO-C can be seamlessly
integrated into the current PPO pipeline and do not require additional golden
labels. We evaluate our methods on both Llama3-8B and Mistral-7B across six
diverse datasets including multiple-choice and open-ended generation.
Experiment results demonstrate that both of our methods can reduce calibration
error and maintain performance comparable to standard PPO. We further show that
they do not compromise model capabilities in open-ended conversation settings.