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About this paper
Xu Zhang, Xunjian Yin, Xiaojun Wan
ContraSolver is a self-alignment algorithm that resolves internal preference contradictions in large language models by systematically transforming cyclic preference graphs into directed acyclic graphs. The method consistently improved LLM performance and alignment across various text generation tasks, including instruction following and summarization, without requiring extensive human feedback.
Key insights
- Large language models can effectively self-align without extensive human feedback by focusing on resolving internal contradictions.
- Maintaining consistent preferences is crucial for effective LLM alignment, as contradictions undermine the assumption of a consistent latent reward function.
- A graph-based approach provides a structured method to analyze, identify, and resolve internal preference inconsistencies in LLMs.
Summary by alphaXiv.
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