arXiv:2606. 24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success.
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arXiv:2606. 11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment.
arXiv:2508. 14390v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often express verbal confidence that is poorly aligned with actual correctness, limiting their reliability in safety-critical applications.
arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
DirEAG introduces a Dirichlet Evidence Aggregation technique to calibrate verbalized confidence in large language models performing mathematical reasoning. By converting each elicited answer-confidence pair into calibrated soft evidence over candidate answers and a null state, it addresses prompt- and task-dependent bias that simple averaging or heuristic aggregation cannot handle. Experiments on GSM8K, SVAMP, and GSM-Hard with Qwen, Mistral, and Gemma models demonstrate that DirEAG achieves better calibration while maintaining competitive answer selection compared to existing methods.
As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important for quantifying the uncertaint...
arXiv:2604.08974v2 Announce Type: replace Abstract: Uncertainty quantification techniques measure confidence in language model outputs to support critical applications like hallucination detection an...