CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

arXiv — cs.CL•Wednesday, November 5, 2025 at 5:00:00 AM

A recent development in the field of large language models is the introduction of Confidence-Guided Early Stopping (CGES), a Bayesian framework designed to enhance self-consistency during prediction. CGES leverages confidence signals to guide the early stopping process, thereby improving both the efficiency and accuracy of model outputs. This approach is particularly effective in scenarios where correct answers are infrequent, addressing a common challenge in language model predictions. By integrating confidence measures, CGES reduces unnecessary computation while maintaining or even boosting the reliability of results. The method represents a significant advancement in optimizing large language models, as documented in recent research shared on arXiv. Its application domain focuses on natural language processing tasks where self-consistency is critical. Overall, CGES offers a promising avenue for balancing computational resources and predictive performance in AI systems.

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