Researchers Propose Geometric Approach to Improve LLM Consistency in Open-Ended Tasks
Computer scientists have developed Embedding-Based Agreement (EBA), a method that treats self-consistency in language models as a geometric property rather than exact matching, allowing it to work on open-ended tasks like code generation and summarization. The approach clusters multiple model outputs in embedding space to identify the most reliable answers without requiring exact matches. This addresses a key limitation of existing self-consistency methods, which only work well for tasks with categorical outputs.
Researchers from the machine learning community have introduced a novel framework for improving language model reasoning on open-ended generation tasks. Traditional self-consistency methods sample multiple outputs and select answers based on exact matching, which works only for categorical tasks. The new Embedding-Based Agreement approach instead treats consistency as a geometric property—the hypothesis that semantically similar generations cluster together in representation space. Through experiments on mathematical reasoning, code generation, and text summarization, the team demonstrated that EBA outperforms random selection and shows more stable scaling than recent LLM evaluation or uncertainty-based approaches. Notably, the method works across different model families and embedding spaces, including native hidden representations. The analysis reveals a strong correlation between geometric location and quality: generations near the center of representation space clusters tend to be more accurate, while peripheral generations are substantially less reliable.
What's missing
The paper does not discuss computational costs or runtime comparisons between EBA and baseline methods. Additionally, while the work covers mathematical reasoning, code generation, and summarization, it does not address potential limitations or failure cases where the geometric clustering hypothesis may not hold.
What different sources said
- arXiv cs.CLCenter
Agreement in Representation Space for Open-Ended Self-Consistency
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