Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning
This addresses the issue of unreliable confidence estimates for users relying on LLMs for complex reasoning, though it is incremental as it builds on existing confidence estimation methods.
The paper tackled the problem of poor confidence calibration in large language models (LLMs) during multi-step reasoning by analyzing how confidence evolves over time using Signal Temporal Logic (STL), resulting in more calibrated confidence scores across multiple reasoning tasks.
Large Language Models (LLMs) increasingly rely on long-form, multi-step reasoning to solve complex tasks such as mathematical problem solving and scientific question answering. Despite strong performance, existing confidence estimation methods typically reduce an entire reasoning process to a single scalar score, ignoring how confidence evolves throughout the generation. As a result, these methods are often sensitive to superficial factors such as response length or verbosity, and struggle to distinguish correct reasoning from confidently stated errors. We propose to characterize the stepwise confidence signal using Signal Temporal Logic (STL). Using a discriminative STL mining procedure, we discover temporal formulas that distinguish confidence signals of correct and incorrect responses. Our analysis found that the STL patterns generalize across tasks, and numeric parameters exhibit sensitivity to individual questions. Based on these insights, we develop a confidence estimation approach that informs STL blocks with parameter hypernetworks. Experiments on multiple reasoning tasks show our confidence scores are more calibrated than the baselines.