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Human feedback is increasingly used to steer the behaviours of Large LanguageModels (LLMs). However, it is unclear how to collect and incorporate feedbackin a way that is efficient, effective and unbiased, especially for highlysubjective human preferences and values. In this paper, we survey existingapproaches for learning from human feedback, drawing on 95 papers primarilyfrom the ACL and arXiv repositories.First, we summarise the past, pre-LLMtrends for integrating human feedback into language models. Second, we give anoverview of present techniques and practices, as well as the motivations forusing feedback; conceptual frameworks for defining values and preferences; andhow feedback is collected and from whom. Finally, we encourage a better futureof feedback learning in LLMs by raising five unresolved conceptual andpractical challenges.

The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values