Calibrating Large Language Models Using Their Generations Only
Parameter Lab（参数实验室）；IT University of Copenhagen（哥本哈根信息技术大学）；Pioneer Centre for Artificial Intelligence（人工智能先锋中心）；NAVER AI Lab（NAVER人工智能实验室）；University of Tübingen（蒂宾根大学）；Tübingen AI Center（蒂宾根人工智能中心）
Dennis Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun, Seong Joon Oh
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a challenge. We propose APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or adjusting the given answer based on the confidence. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.
Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学吴贤铭智能工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；University of Electronic Science and Technology of China（电子科技大学）
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
School of Computational Science \& Engineering Georgia Institute of Technology Atlanta GA USA；Scheller College of Business Georgia Institute of Technology Atlanta GA USA；Georgia Institute of Technology
PRISM: Calibrating Black-Box Judges with Counterfactual Probes
Massachusetts Institute of Technology（麻省理工学院）；Sakana AI；Lab for Information and Decision Systems（信息与决策系统实验室）；Statistics and Data Science Center（统计与数据科学中心）；EECS（电气工程与计算机科学系）
PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Lingxin AI（灵心AI）；South China Normal University（华南师范大学）；CoAI Group, DCST, IAI, BNRIST, Tsinghua University（清华人工智能研究院）
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）
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA（加州大学圣地亚哥分校计算机科学与工程系，拉贾尔，加利福尼亚州，美国）；Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California, USA（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所，拉贾尔，加利福尼亚州，美国）；Department of Statistics, Stanford University, Stanford, California, USA（斯坦福大学统计学系，斯坦福，加利福尼亚州，美国）
Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）
On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning
Department of Computer Science, TU Dortmund University（图恩-多特蒙德大学计算机科学系）；Bonn-Aachen International Center for Information Technology, University of Bonn（波恩-亚琛国际信息科技中心，波恩大学）；Computer Science School, University of Sheffield（谢菲尔德大学计算机科学学院）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
