Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequently used chat template's effect on the calibration of conversational LLMs. In this work, we investigate the mechanisms driving this miscalibration by decoupling the effects of the post-training algorithm and the chat format. We find that, while instruction tuning fundamentally harms calibration, the chat template aggravates the issue through an "ownership bias" -- models are significantly more confident in their own answers than in identical answers provided by a user. Extensive experiments across six recent open-weight LLMs, three benchmarks, and three confidence elicitation methods show that models assign up to 26% higher confidence to their own responses. Leveraging this insight, we propose a simple inference-time strategy: framing the model's answer as user input during confidence elicitation. This approach significantly reduces overconfidence and improves calibration by up to 26% without the need for retraining, narrowing the gap between base and instruction-tuned models.
Can LLM Rerankers Predict Their Own Ranking Performance?
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）
Beyond the Surface: Measuring Self-Preference in LLM Judgments
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学首都人工智能学院）；Huawei Poisson Lab（华为Poisson实验室）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理重点实验室）
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电子工程与计算机科学系）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
Inducing language models to assert their own consciousness restores human beliefs and values
Google（谷歌）；University of Chicago（芝加哥大学）；University of London（伦敦大学）；University of Washington（华盛顿大学）；Northwestern University（西北大学）；Santa Fe Institute（圣达菲研究所）
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Tencent AI Lab（腾讯AI实验室）；Tencent Multimodal Department（腾讯多模态部门）；University of North Carolina at Chapel Hill（北卡罗来纳大学夏洛特分校）；University of Virginia（弗吉尼亚大学）；University of Maryland, College Park（马里兰大学学院公园分校）
CREAM: Consistency Regularized Self-Rewarding Language Models
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Nanyang Technological University（南洋理工大学）；National University of Singapore（国立新加坡大学）；Microsoft Research（微软研究院）
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
DeepMind, London, UK（深度思维公司，伦敦，英国）；Google Research, New York, NY, USA（谷歌研究院，纽约，纽约州，美国）；Stanford University, Stanford, CA, USA（斯坦福大学，斯坦福，加利福尼亚州，美国）；University of Toronto, Toronto, Ontario, Canada（多伦多大学，多伦多，安大略省，加拿大）；University of Washington, Seattle, WA, USA（华盛顿大学，西雅图，华盛顿州，美国）
Listener's Social Identity Matters in Personalised Response Generation
Department of Information and Computing Sciences, Utrecht University（乌得勒支大学信息与计算科学系）；Samsung Research China - Beijing (SRC-B)（三星中国研究院-北京(SRC-B)）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
