Fine-Grained Human Feedback Gives Better Rewards for Language Model Training (2306.01693v2)

Abstract: LLMs (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a learning signal - has recently shown promise in addressing these issues. However, such holistic feedback conveys limited information on long text outputs; it does not indicate which aspects of the outputs influenced user preference; e.g., which parts contain what type(s) of errors. In this paper, we use fine-grained human feedback (e.g., which sentence is false, which sub-sentence is irrelevant) as an explicit training signal. We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e.g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e.g., factual incorrectness, irrelevance, and information incompleteness). We conduct experiments on detoxification and long-form question answering to illustrate how learning with such reward functions leads to improved performance, supported by both automatic and human evaluation. Additionally, we show that LM behaviors can be customized using different combinations of fine-grained reward models. We release all data, collected human feedback, and codes at https://FineGrainedRLHF.github.io.

Fine-Grained Human Feedback in LLM Training: An Analytical Exploration

The exploration of LLMs (LMs) in recent years has unveiled significant challenges, such as generating outputs that are false, toxic, or irrelevant. The paper "Fine-Grained Human Feedback Gives Better Rewards for LLM Training" addresses these issues by presenting a new framework that leverages fine-grained human feedback to improve LLM outputs. This approach, termed Fine-Grained RLHF (Reinforcement Learning from Human Feedback), is analyzed across different dimensions, showing its efficacy over more traditional holistic feedback methods.

Key Contributions and Method

The primary contribution of the paper is the introduction of Fine-Grained RLHF, a framework designed to train LLMs using reward functions derived from fine-grained human feedback. This method focuses on two essential aspects:

1. Reward Density: Fine-Grained RLHF provides a reward signal after every segment (e.g., sentence) of text is generated, rather than at the end of the entire output text. This increased reward frequency potentially enhances the sample efficiency of the reinforcement learning process.
2. Multiple Reward Models: The framework incorporates multiple reward models, each associated with different feedback types such as factual incorrectness, irrelevance, and information incompleteness. This allows for a nuanced approach where different aspects of text quality are separately assessed and optimized.

The framework operates within a Reinforcement Learning (RL) paradigm, integrating rewards from multiple specialized models into Proximal Policy Optimization (PPO). In contrast to previous RLHF methods that rely on a single scalar reward per sequence, Fine-Grained RLHF optimizes the model's behavior specifically across multiple dimensions of text quality simultaneously.

Experimental Evaluation

The authors evaluate the Fine-Grained RLHF approach on two distinct tasks: detoxification and long-form question answering (QA).

• Detoxification: The paper uses the RealToxicityPrompts dataset, employing fine-grained reward models at the sentence level to reduce toxicity. The results demonstrate that Fine-Grained RLHF outperforms holistic RLHF and other detoxification methods, showing a marked improvement in toxicity reduction with faster convergence and higher sample efficiency.
• Long-Form QA: For long-form QA, the authors introduce a constructed dataset called QA-Feedback, which is annotated with fine-grained feedback across three categories of errors. Experiments with T5-based models reveal that Fine-Grained RLHF provides superior outcomes concerning factual accuracy, relevance, and completeness compared to preference-based RLHF and supervised models. The paper further investigates fine-tuning reward model weights to customize LM behavior, catering to varying user needs for conciseness versus completeness.

Implications and Future Directions

The implications of this work are profound for both practical applications and theoretical advancements in AI. Fine-Grained RLHF not only enhances the immediate performance of LMs by reducing undesirable outputs but also offers a pathway towards more customizable AI systems that can be tuned for specific applications, such as education or customer service, by adjusting reward model weights. Moreover, it suggests a more granular approach to learning from feedback, potentially applicable to other AI domains where nuanced performance characteristics are critical.

Future development could explore more scalable methods of acquiring fine-grained feedback, potentially leveraging automated systems or models to simulate human feedback, which would curb the resource intensity of broad feedback collection. Additionally, investigating the integration of fine-grained feedback into downstream tasks beyond LLMing could elucidate its broader applicability.

Conclusion

This paper contributes a novel framework for LLM training, addressing critical issues of false, toxic, or irrelevant outputs by employing a fine-grained approach to human feedback. The proposed method demonstrates clear advantages in producing more accurate, relevant, and safe LLM outputs, charting a path forward for future research in customizable and reliable AI systems.