RLHF’s Optimization Assumption of LLM under Web3 Incentive Mechanism

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RLHF’s Optimization Assumption of LLM under Web3 Incentive Mechanism

Abstract

This article explores the potential and strategies of utilizing Web3 incentive mechanisms to optimize large language models (LLMs). By conducting an in-depth analysis of the concept, principles, and application of Reinforcement Learning from Human Feedback (RLHF) in LLMs, this study aims to address how incentive mechanisms can improve the quality of human feedback, thereby enhancing the performance and accuracy of language models. The article begins by outlining the basics of RLHF, including its relationship with traditional reinforcement learning, advantages, and existing limitations. Subsequently, the paper proposes a series of ideas for employing Web3 incentive mechanisms, aimed at encouraging users to provide higher quality and more honest feedback through economic rewards. These proposals include methods for determining the sincerity of suggested replies and rating feed-back, reward calculation mechanisms, and strategies for enhancing rating judgments using implicit
feedback.

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