Safer-Instruct: Aligning Language Models with Automated Preference Data (2311.08685v3)

Abstract: Reinforcement learning from human feedback (RLHF) is a vital strategy for enhancing model capability in LLMs. However, annotating preference data for RLHF is a resource-intensive and creativity-demanding process, while existing automatic generation methods face limitations in data diversity and quality. In response, we present Safer-Instruct, a novel pipeline for automatically constructing large-scale preference data. Our approach leverages reversed instruction tuning, instruction induction, and expert model evaluation to efficiently generate high-quality preference data without human annotators. To verify the effectiveness of Safer-Instruct, we apply the pipeline to construct a safety preference dataset as a case study. Finetuning an Alpaca model on this synthetic dataset not only demonstrates improved harmlessness but also outperforms models fine-tuned on human-annotated safety preference data, all the while maintaining a competitive edge in downstream tasks. Importantly, our Safer-Instruct framework is versatile and can be applied to generate preference data across various domains, extending its utility beyond safety preferences. It addresses the challenges in preference data acquisition and advances the development of more capable and responsible AI systems. For dataset and code implementation, see https://github.com/uscnlp-lime/safer-instruct

Safer-Instruct: Aligning LLMs with Automated Preference Data

The paper "Safer-Instruct: Aligning LLMs with Automated Preference Data" introduces a novel framework designed to enhance the efficiency and quality of reinforcement learning from human feedback (RLHF) in training LLMs. This framework, Safer-Instruct, aims to generate large-scale preference data automatically, circumventing the high costs and resource demands typically associated with the human annotation of such data.

Core Contributions

Safer-Instruct employs a range of techniques to construct preference datasets effectively. These include reversed instruction tuning, instruction induction, and model evaluation by an expert AI system. The reversed instruction tuning approach enables the generation of diverse and contextually relevant instructions from existing responses. Instruction induction serves to convert these responses into valuable training instructions without relying on manual input.

The framework's efficacy is demonstrated through the creation of a safety preference dataset, which is then used to fine-tune an Alpaca model—a variant of the LLaMA-based models. Results show that the model trained on data generated by Safer-Instruct exhibits superior harmlessness compared to models fine-tuned with human-annotated datasets. This enhancement in safety is achieved without compromising the model's competency in downstream tasks and overall dialogue abilities.

Significant Findings

The authors report that the Alpaca model fine-tuned on the Safer-Instruct-generated dataset surpasses the safety performance of models trained on Anthropic's Helpfulness and Harmless (HH) dataset, Beaver Tail, and to some extent, even GPT-4. By benchmarking these results, the paper demonstrates the potential of automated preference data generation frameworks to improve AI system responsibilities.

Furthermore, the paper identifies weaknesses in current methodologies, such as the limited scope of existing automated instruction generation methods and the challenge posed by robust safety mechanisms in expert models like GPT-4, which prevent creating certain dispreferred content.

Implications and Future Directions

Practically, Safer-Instruct presents a versatile approach suitable for various domains beyond safety, addressing significant challenges in the acquisition of high-quality preference data. Theoretically, it offers a new perspective on bottlenecks in RLHF processes, emphasizing an automated, scalable response to data diversity and quality issues.

Looking ahead, the research opens avenues for deploying more capable and responsible AI systems that align better with human values and ethics—an essential consideration as AI technologies increasingly permeate societal infrastructure. The paper also suggests future exploration into more nuanced response-generation techniques that could address complex or sensitive user queries more effectively, moving beyond simplistic refusals.

Conclusion

The introduction of Safer-Instruct provides a substantial advancement in the field of AI alignment, offering an efficient, reliable, and cost-effective solution to the challenges of RLHF via automated data generation. This promising framework not only showcases the potential for improving AI safety but also sets a precedent for future innovations aimed at refining AI alignment processes.