Title: WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: safety and alignment, automatic creation and evaluation of language resources, NLP datasets, automatic evaluation of datasets, evaluation methodologies, evaluation

Abstract: As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive nature, inherent subjectivity, misalignment with real-world user preferences, and the risk of feedback loops that amplify model biases. To overcome these limitations, we introduce WildFeedback, a novel framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. Given a corpus of multi-turn user-LLM conversation, WildFeedback identifies and classifies user feedback to LLM responses between conversation turns. The user feedback is then used to create examples of preferred and dispreferred responses according to users' preference. Our experiments demonstrate that LLMs fine-tuned on WildFeedback dataset exhibit significantly improved alignment with user preferences, as evidenced by both traditional benchmarks and our proposed checklist-guided evaluation. By incorporating in-situ feedback from actual users, WildFeedback addresses the scalability, subjectivity, and bias challenges that plague existing approaches, marking a significant step toward developing LLMs that are more responsive to the diverse and evolving needs of their users.

TL;DR: WildFeedback leverages in-situ user feedback from conversations with LLMs to automatically construct preference datasets, improving alignment with real-world user needs and outperforming traditional annotation methods.