Active Human Feedback Collection via Neural Contextual Dueling Bandits
This work addresses the costly and inefficient collection of human feedback for applications like recommendation systems and AI alignment, offering a novel method for non-linear contexts, though it builds on existing bandit frameworks.
The paper tackles the problem of efficiently collecting human preference feedback in non-linear reward settings, such as online recommendation and LLM alignment, by proposing Neural-ADB, an algorithm based on neural contextual dueling bandits, which theoretically reduces the worst sub-optimality gap at a sub-linear rate and shows effectiveness in experiments.
Collecting human preference feedback is often expensive, leading recent works to develop principled algorithms to select them more efficiently. However, these works assume that the underlying reward function is linear, an assumption that does not hold in many real-life applications, such as online recommendation and LLM alignment. To address this limitation, we propose Neural-ADB, an algorithm based on the neural contextual dueling bandit framework that provides a principled and practical method for collecting human preference feedback when the underlying latent reward function is non-linear. We theoretically show that when preference feedback follows the Bradley-Terry-Luce model, the worst sub-optimality gap of the policy learned by Neural-ADB decreases at a sub-linear rate as the preference dataset increases. Our experimental results on preference datasets further corroborate the effectiveness of Neural-ADB.