Training language models to follow instructions with human feedback
Abstract
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of InstructGPT, a language model fine-tuned with human feedback that significantly improves alignment with user intent across a variety of tasks. This work not only advances the state of the art in language model training but also provides a framework for future research on aligning AI systems with human values and preferences.
Methodology
The paper presents a well-structured methodology for fine-tuning language models using human feedback through reinforcement learning. It combines supervised learning with reinforcement learning from human feedback (RLHF) to align model outputs with user intent effectively. The approach is systematic, involving data collection from labelers, training a reward model, and optimizing the language model using Proximal Policy Optimization (PPO). This iterative process allows for continuous improvement and adaptation of the model to user preferences. The methodology is clearly articulated, though it heavily relies on the quality of human feedback, which may introduce variability.
Results & evaluation
The experiments are robust, employing a variety of evaluation metrics, including human preference ratings and performance on public NLP datasets. The results demonstrate significant improvements in truthfulness, reduced toxicity, and overall user satisfaction compared to the baseline GPT-3 model. The paper also provides a thorough analysis of the model's performance across different tasks and datasets, showcasing the effectiveness of the proposed fine-tuning approach. However, the reliance on labeler evaluations may limit the generalizability of the findings.
Reproducibility
The paper provides sufficient details about the training process, datasets, and evaluation metrics, which would allow for reproducibility of the experiments. The authors mention the use of specific model architectures and training techniques, and they provide links to their project repository, which is a positive aspect for reproducibility. However, the paper could benefit from more extensive documentation of the experimental setup and hyperparameters used.
Limitations
The paper acknowledges several limitations, including the potential for "alignment tax," where fine-tuning may lead to performance regressions on certain public NLP datasets. Additionally, the model still makes simple mistakes, such as failing to follow instructions correctly or generating inaccurate information. The evaluation primarily focuses on labeler preferences, which may not fully capture user intent across diverse populations. There is also a need for further exploration of the model's performance on broader user groups and more complex tasks.
Broader impact
The research has significant implications for the development of more aligned and user-friendly AI systems. By improving the ability of language models to follow user instructions and generate safer outputs, the work contributes to addressing ethical concerns surrounding AI deployment. The findings could influence future research directions in alignment and safety, as well as practical applications in various domains, including customer service, content generation, and education. The main contribution of this paper is the introduction of InstructGPT, a language model fine-tuned with human feedback that significantly improves alignment with user intent across a variety of tasks. This work not only advances the state of the art in language model training but also provides a framework for future research on aligning AI systems with human values and preferences.
Curator note
Ouyang et al.; RLHF for LLMs; precursor to ChatGPT
Talks & videos
Related later work
- Constitutional AI 2022
- Direct Preference Optimization (DPO) 2023
- Llama 2 Chat (RLHF) 2023
- Self-Rewarding Language Models 2024
- RLAIF (AI Feedback) 2023
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