In this paper, we consider the extent to which the transformer-based Dense Passage Retrieval (DPR) algorithm, developed by (Karpukhin et. al. 2020), can be optimized without further pre-training. Our method involves two particular insights: we apply the DPR context encoder at various phrase lengths (e.g. one-sentence versus five-sentence segments), and we take a confidence-calibrated ensemble prediction over all of these different segmentations. This somewhat exhaustive approach achieves start-of-the-art results on benchmark datasets such as Google NQ and SQuAD. We also apply our method to domain-specific datasets, and the results suggest how different granularities are optimal for different domains
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Calibrated Confidence Expression for Radiology Report Generation
Computer Aided Medical Procedures, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗程序）；Munich Center for Machine Learning (MCML), Germany（德国慕尼黑机器学习中心）；Department of Diagnostic and Interventional Radiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入放射科）；Department of Diagnostic and Interventional Neuroradiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入神经放射科）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany（德国汉堡-埃彭多夫大学医学中心诊断与介入放射学及核医学科）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany（德国慕尼黑工业大学人工智能图像引导诊断与治疗）
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹大学医学信息学系，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Amsterdam Public Health, Methodology, Amsterdam, The Netherlands（阿姆斯特丹公共卫生，方法学，阿姆斯特丹，荷兰）
Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China（中山大学计算机科学与工程学院，广州，中国）；Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学计算机系，香港特别行政区）；School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR（香港 Metropolitan 大学科技学院，香港特别行政区）
C$^2$GSPG: Confidence-calibrated Group Sequence Policy Gradient towards Self-aware Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）；Tsinghua University（清华大学）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
