Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?

Kristian González Barman, Simon Lohse, Henk de Regt
7/2/2024
cs.CYcs.AIcs.CL

No confident code implementation found for this paper.

Abstract

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

88% match2024

Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback

87% match2023

Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond

87% match2023

Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework

86% match2024

Curiosity-Driven Reinforcement Learning from Human Feedback

86% match1 repo162025

Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning

86% match1 repo82025

Cite this paper

@article{barman2024reinforcement,
  title  = {Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?},
  author = {Kristian González Barman and Simon Lohse and Henk de Regt},
  year   = {2024},
  eprint = {2407.17482},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.17482v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.17482.svg)](https://codeofpaper.com/paper/2407.17482)

Discussion