Reinforcement learning with human feedback (RLHF) is a cutting-edge technique that has been gaining popularity in recent years as a means of improving the performance of large language models. It’s a powerful way to train these models using human feedback, and the human input component has many similarities to search evaluation. Both approaches are designed to improve the quality and relevance of the output through the use of subjective human input. While for search evaluation, humans focus on ranking search results, as part of RLHF, however, humans focus on generating natural language prompts, responses that are representative of an input prompt, and preference rankings of those responses.
At Appen, we have deep expertise in delivering large-scale data for search relevance and are now applying our search expertise to support the growth of generative AI models through RLHF. We have worked with many clients on improving the performance of large language models, and we see a close alignment between RLHF and our mission to help companies create high-quality, relevant content that engages users.
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