AI Alignment through RLHF
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This paper provides critical analysis of RLHF/RLAIF alignment methods, examining limitations in common alignment goals (honesty, harmlessness, helpfulness) through a sociotechnical lens, directly addressing core AI alignment challenges.
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Abstract
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback (RLxF) methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinnings and practical implementations of RLxF techniques, revealing significant limitations in their approach to capturing the complexities of human ethics and contributing to AI safety. We highlight tensions and contradictions inherent in the goals of RLxF. In addition, we discuss ethically-relevant issues that tend to be neglected in discussions about alignment and RLxF, among which the trade-offs between user-friendliness and deception, flexibility and interpretability, and system safety. We conclude by urging researchers and practitioners alike to critically assess the sociotechnical ramifications of RLxF, advocating for a more nuanced and reflective approach to its application in AI development.
Summary
This paper provides a critical sociotechnical analysis of Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF) as alignment approaches for large language models. The authors argue that while RLHF aims to achieve honesty, harmlessness, and helpfulness, these methods have significant theoretical and practical limitations in capturing the complexity of human ethics and ensuring genuine AI safety. The paper identifies inherent tensions in alignment goals and highlights neglected ethical issues, ultimately calling for a more nuanced and reflective approach to RLxF implementation in AI development.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| Is Interpretability Sufficient for Safety? | Crux | 49.0 |
| AI Safety Intervention Portfolio | Approach | 91.0 |
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[2406.18346] AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations
AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations
Adam Dahlgren Lindström
Department of Computing Science
Umeå University
dali@cs.umu.se
\And Leila Methnani
Department of Computing Science
Umeå University
leilam@cs.umu.se
\And Lea Krause
Computational Linguistics and
Text Mining Lab
Vrije Universiteit Amsterdam
l.krause@vu.nl
\And Petter Ericson
Department of Computing Science
Umeå University
pettter@cs.umu.se
\And Íñigo Martínez de Rituerto de Troya
Department of Engineering
Systems and Services
TU Delft
i.m.d.r.detroya@tudelft.nl
\And Dimitri Coelho Mollo
Department of Historical,
Philosophical, and Religious Studies
Umeå University
dimitri.mollo@umu.se
\And Roel Dobbe
Department of Engineering
Systems and Services
TU Delft
r.i.j.dobbe@tudelft.nl
Abstract
This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback (RLxF) methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinnings and practical implementations of RLxF techniques, revealing significant limitations in their approach to capturing the complexities of human ethics and contributing to AI safety. We highlight tensions and contradictions inherent in the goals of RLxF. In addition, we discuss ethically-relevant issues that tend to be neglected in discussions about alignment and RLxF, among which the trade-offs between user-friendliness and deception, flexibility and interpretability, and system safety. We conclude by urging researchers and practitioners alike to critically assess the sociotechnical ramifications of RLxF, advocating for a more nuanced and reflective approach to its application in AI development.
1 Introduction
We chose ‘helpful, honest, and harmless’ as criteria because they are simple and memorable, and seem to capture the majority of what we want from an aligned AI.
[ ? ]
Reinforcement Learning from Human Feedback (RLHF) presents itself as a straightforward method for ensuring Artificial Intelligence (AI) oversight [ ? ] and AI safety through value alignment.
It has recently played a large role in improving Large Language Model (LLM) performance, with fine-tuning using RLHF intended to produce more ‘natural-sounding’ text, generating plausible conversational responses in a chatbot-like setting.
It is often claimed by AI companies and researchers that RLHF fine-tuning ensures that the LLMs
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