Key Points
1. Alignment is the most critical step in building large language models (LLMs) that meet human needs. However, traditional alignment methods based on human annotation are increasingly unable to meet the scalability demands as LLMs surpass human capabilities.
2. This survey systematically reviews the recently emerging methods of automated alignment, attempting to explore how to achieve effective, scalable, automated alignment as LLM capabilities exceed those of humans.
3. The survey categorizes existing automated alignment methods into 4 major categories based on the sources of alignment signals: aligning through inductive bias, behavior imitation, model feedback, and environment feedback.
4. Aligning through inductive bias involves enhancing models with assumed generalizations or rules, enabling them to produce better-aligned responses without explicit external guidance.
5. Aligning through behavior imitation aims to align the behaviors of a target model with those of a teacher model through imitation, including strong-to-weak distillation and weak-to-strong alignment.
6. Aligning through model feedback involves guiding the alignment optimization of the target model by obtaining feedback from other models, including scalar, binary, and text signals.
7. Aligning through environment feedback aims to automatically obtain alignment signals or feedback through interaction with the environment, such as social interactions, human collective intelligence, tool execution, and embodied environments.
8. The survey also explores the underlying mechanisms that enable automated alignment, including the fundamental role of alignment, the reasons behind why self-feedback works, and the feasibility of weak-to-strong generalization.
9. The survey concludes by highlighting the research gaps and calling for future efforts to bridge these gaps, ensuring the safe and effective application of LLMs in real-world scenarios.
Summary
The Need for Automated Alignment Approaches
The paper highlights the growing need for automated alignment approaches as the capabilities of LLMs rapidly surpass those of humans. Traditional human-annotation methods are becoming increasingly limited in their ability to effectively align these powerful models with human values and needs.
The paper categorizes the emerging methods for automated alignment into four main approaches:
Beyond reviewing these four technical approaches, the paper also delves into the underlying mechanisms of automated alignment. It explores three key research questions:
In conclusion, this survey highlights the substantial progress in automated alignment techniques, but also identifies critical research gaps in understanding the underlying mechanisms. Addressing these gaps, particularly around self-feedback reliability and weak-to-strong generalization, is essential for further advancing the development of safe, effective, and scalable AI systems aligned with human values. The paper advocates for increased efforts to bridge the theoretical and empirical understanding of automated alignment, which can inform the design of more robust and trustworthy large language models.