Preference Ranking Optimization for Human Alignment with Large Language Models
By Feifan Song et al.
Word Count: 5,841
Estimated Read Time: 26 - 43 minutes
The paper proposes a method called Preference Ranking Optimization (PRO) for aligning large language models with human preferences. The key idea is that human alignment can be modeled as aligning the probability ranking of responses generated by the language model with the preference ranking of human annotators. PRO directly optimizes the language model to learn this ranking, avoiding the complexity of reinforcement learning approaches.
The authors evaluate PRO on a dataset of human feedback and find that it outperforms baselines in aligning with human preferences, achieving similar results to ChatGPT. They also analyze factors that influence the performance of PRO, such as the length and diversity of the preference rankings.
In summary, PRO presents an effective and efficient method for aligning language models to human values. With further research, similar techniques could improve the safety, trustworthiness and interpretability of large language models.
However, PRO relies on heuristics rather than formalizing human preferences as a well-defined objective. This may limit the generalizability of the approach. Additionally, the paper focuses on conversational data, so it remains to be seen how well PRO can align language models for other tasks.