Aligning Large Language Models through Synthetic Feedback (2305.13735v2)

Abstract: Aligning LLMs to human values has become increasingly important as it enables sophisticated steering of LLMs. However, it requires significant human demonstrations and feedback or distillation from proprietary LLMs such as ChatGPT. In this work, we propose a novel alignment learning framework with synthetic feedback not dependent on extensive human annotations and proprietary LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM to simulate high-quality demonstrations to train a supervised policy and further optimize the model with reinforcement learning. Our resulting model, Aligned LLM with Synthetic Training dataset (ALMoST), outperforms recent open-sourced models, which are trained on the outputs of InstructGPT or human-annotated demonstrations, in alignment benchmarks. In human evaluation, our model is preferred to Alpaca and Dolly-v2, 55.0% and 58.5% of the time, respectively. Further analyses demonstrate the efficacy and importance of synthetic feedback in our framework. The code is available at https://github.com/naver-ai/almost

Aligning LLMs through Synthetic Feedback

The paper "Aligning LLMs through Synthetic Feedback" introduces a novel framework to align LLMs with human values without relying extensively on human-annotated data or proprietary models. This research is particularly significant given the growing importance of aligning AI models with ethical guidelines and user preferences to ensure they perform safely and helpfully.

The proposed framework addresses the need for alignment by generating synthetic feedback to guide the training process. The authors build their approach around three main stages: synthetic reward modeling (RM), supervised fine-tuning with simulated demonstrations, and reinforcement learning from synthetic feedback (RLSF).

Reward Modeling with Synthetic Feedback

The paper outlines a novel approach to reward modeling, wherein synthetic feedback is generated by contrasting outputs from vanilla LLMs configured with different sizes and prompts. This approach eliminates the need for human demonstrations typically required in traditional alignment learning frameworks. The synthetic comparisons are based on the assumption that larger, better-prompted models outperform smaller, less-optimized ones. To ensure the quality of the synthetic data, the authors employ heuristic filtering and validate the dataset using a reward model pre-trained on community-contributed data.

Supervised Fine-Tuning and Reinforcement Learning

Following the creation of a reward model, the framework generates high-quality simulated demonstrations using a reward-model-guided self-play strategy, which involves the rejection sampling of the most aligned responses. The LLM is then fine-tuned in a supervised manner with these demonstrations. The final optimization is carried out using reinforcement learning, where the LLM's policy is updated based on synthetic reward signals, further refining the alignment with desired human values.

Empirical Results and Comparisons

The model resulting from this framework, termed ALMoST (Aligned LLM with Synthetic Training dataset), demonstrated competitive or superior performance in alignment benchmarks compared to models trained with human-annotated data or outputs from proprietary LLMs. ALMoST outperformed other open-source models such as Alpaca and Dolly in alignment-related evaluation metrics, achieving a human preference rate of over 55% in comparisons. The reward model also showed a strong capacity to filter and select highly aligned responses, performing better than many alternatives in alignment evaluations.

Implications and Future Directions

The framework's ability to surpass traditional methods using synthetic feedback indicates a significant shift in how alignment can be achieved in LLMs. This approach reduces dependence on costly and labor-intensive human feedback or distillation from proprietary models, unlocking potential for more efficient and accessible alignment processes.

The implications of this work are broad. Practically, it sets a precedent for developing AI systems that are more aligned with human values while being resource-efficient. Theoretically, it advances the understanding of how LLMs can self-regulate and potentially overcome some of the challenges associated with scaling human feedback mechanisms.

Future research could expand on this work by exploring the scalability of synthetic feedback approaches across various domains and languages, and further probing the balance between alignment and the preservation of a model’s other capabilities to address the alignment tax. Additionally, extending this methodology to integrate more sophisticated metrics for aligning models with nuanced ethical standards could enhance its applicability.

In conclusion, this paper presents a compelling case for synthetic feedback as a viable tool for aligning LLMs with human values, offering a pathway to both more aligned and resource-efficient AI systems.