Online Learning from Strategic Human Feedback in LLM Fine-Tuning
This addresses a critical issue in RLHF for aligning LLMs with human preferences, though it appears incremental as it builds on existing RLHF frameworks.
The paper tackles the problem of strategic human labelers misreporting feedback in LLM fine-tuning, which causes linear regret, by proposing a dynamic Bayesian game mechanism that adjusts labeler weights to ensure truthful feedback and achieves sublinear regret O(T^{1/2}).
Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system's aggregation towards their own preferences. Current practice simply averages labelers' feedback per time and fails to identify the most accurate human labeler, leading to linear regret $\mathcal{O}(T)$ for $T$ time slots. To our best knowledge, we are the first to study online learning mechanisms against strategic human labelers in the LLM fine-tuning process. We formulate a new dynamic Bayesian game and dynamically adjust human labelers' weights in the preference aggregation, ensuring their truthful feedback and sublinear regret $\mathcal{O}(T^{1/2})$. Simulation results demonstrate our mechanism's great advantages over the existing benchmark schemes.