@inproceedings{kirk-etal-2023-past,
title = "The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values",
author = "Kirk, Hannah and
Bean, Andrew and
Vidgen, Bertie and
Rottger, Paul and
Hale, Scott",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.148",
doi = "10.18653/v1/2023.emnlp-main.148",
pages = "2409--2430",
abstract = "Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human preferences and values. In this paper, we survey existing approaches for learning from human feedback, drawing on 95 papers primarily from the ACL and arXiv repositories. First, we summarise the past, pre-LLM trends for integrating human feedback into language models. Second, we give an overview of present techniques and practices, as well as the motivations for using feedback; conceptual frameworks for defining values and preferences; and how feedback is collected and from whom. Finally, we encourage a better future of feedback learning in LLMs by raising five unresolved conceptual and practical challenges.",
}
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<abstract>Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human preferences and values. In this paper, we survey existing approaches for learning from human feedback, drawing on 95 papers primarily from the ACL and arXiv repositories. First, we summarise the past, pre-LLM trends for integrating human feedback into language models. Second, we give an overview of present techniques and practices, as well as the motivations for using feedback; conceptual frameworks for defining values and preferences; and how feedback is collected and from whom. Finally, we encourage a better future of feedback learning in LLMs by raising five unresolved conceptual and practical challenges.</abstract>
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%0 Conference Proceedings
%T The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values
%A Kirk, Hannah
%A Bean, Andrew
%A Vidgen, Bertie
%A Rottger, Paul
%A Hale, Scott
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kirk-etal-2023-past
%X Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and incorporate feedback in a way that is efficient, effective and unbiased, especially for highly subjective human preferences and values. In this paper, we survey existing approaches for learning from human feedback, drawing on 95 papers primarily from the ACL and arXiv repositories. First, we summarise the past, pre-LLM trends for integrating human feedback into language models. Second, we give an overview of present techniques and practices, as well as the motivations for using feedback; conceptual frameworks for defining values and preferences; and how feedback is collected and from whom. Finally, we encourage a better future of feedback learning in LLMs by raising five unresolved conceptual and practical challenges.
%R 10.18653/v1/2023.emnlp-main.148
%U https://aclanthology.org/2023.emnlp-main.148
%U https://doi.org/10.18653/v1/2023.emnlp-main.148
%P 2409-2430
Markdown (Informal)
[The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values](https://aclanthology.org/2023.emnlp-main.148) (Kirk et al., EMNLP 2023)
ACL