4 papers
Correcting Large Language Model Behavior via Influence Function
Han Zhang, Zhuo Zhang, Yi Zhang +8
Recent advancements in AI alignment techniques have significantly improved the alignment of large language models (LLMs) with static human preferences. However, the dynamic nature…
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration
Ang Li, Jingqian Zhao, Bin Liang +6
Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements…
COPR: Continual Human Preference Learning via Optimal Policy Regularization
Han Zhang, Lin Gui, Yu Lei +8
Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences. Given the evolving nature of…
COPR: Continual Learning Human Preference through Optimal Policy Regularization
Han Zhang, Lin Gui, Yuanzhao Zhai +3
The technique of Reinforcement Learning from Human Feedback (RLHF) is a commonly employed method to improve pre-trained Language Models (LM), enhancing their ability to conform to…