Public articles linked to the same research event.
arXiv The work proposes Test-Time Calibration Learning (TTCL), a label-free framework that derives self-supervision signals for correctness and calibration from multiple model-generated responses and jointly adapts reasoning accuracy and verbalized confidence on unlabeled target-task data, reporting consistent improvements in both accuracy and calibration on mathematical reasoning and factual question answering.
The work proposes Test-Time Calibration Learning (TTCL), a label-free framework that derives self-supervision signals for correctness and calibration from multiple model-generated responses and jointly adapts reasoning accuracy and verbalized confidence on unlabeled target-task data, reporting consistent improvements in both accuracy and calibration on mathematical reasoning and factual question answering.
The work proposes Test-Time Calibration Learning (TTCL), a label-free framework that derives self-supervision signals for correctness and calibration from multiple model-generated responses and jointly adapts reasoning accuracy and verbalized confidence on unlabeled target-task data, reporting consistent improvements in both accuracy and calibration on mathematical reasoning and factual question answering.
The work proposes Test-Time Calibration Learning (TTCL), a label-free framework that derives self-supervision signals for correctness and calibration from multiple model-generated responses and jointly adapts reasoning accuracy and verbalized confidence on unlabeled target-task data, reporting consistent improvements in both accuracy and calibration on mathematical reasoning and factual question answering.