As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation. Yet, these preferences merely reflect broad tendencies, resulting in less explainable and controllable models with potential safety risks. In this work, we dissect the preferences of human and 32 different LLMs to understand their quantitative composition, using annotations from real-world user-model conversations for a fine-grained, scenario-wise analysis. We find that humans are less sensitive to errors, favor responses that support their stances, and show clear dislike when models admit their limits. On the contrary, advanced LLMs like GPT-4-Turbo emphasize correctness, clarity, and harmlessness more. Additionally, LLMs of similar sizes tend to exhibit similar preferences, regardless of their training methods, and fine-tuning for alignment does not significantly alter the preferences of pretrained-only LLMs. Finally, we show that preference-based evaluation can be intentionally manipulated. In both training-free and training-based settings, aligning a model with the preferences of judges boosts scores, while injecting the least preferred properties lowers them. This results in notable score shifts: up to 0.59 on MT-Bench (1-10 scale) and 31.94 on AlpacaEval 2.0 (0-100 scale), highlighting the significant impact of this strategic adaptation. Interactive Demo: https://huggingface.co/spaces/GAIR/Preference-Dissection-Visualization Dataset: https://huggingface.co/datasets/GAIR/preference-dissection Code: https://github.com/GAIR-NLP/Preference-Dissection
Dissecting Role Cognition in Medical LLMs via Neuronal Ablation
School of Information, Renmin University of China（中国人民大学信息学院）；Center of Machine Learning Research, Peking University（北京大学机器学习研究中心）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）；Health Informatics Committee of the China Society for Scientific and Technical Information, Renmin University of China（中国科学技术信息学会健康信息技术委员会，中国人民大学）；Institute for Advanced Algorithms Research (IAAR), Shanghai, China（先进算法研究所（IAAR），上海，中国）；Memtensor Research Center, Shanghai, China（Memtensor研究中心，上海，中国）
Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?
Department of Psychology, 1202 W. Johnson Street Madison, WI 53706 USA（心理学系）；Department of Educational Psychology, 1025 W. Johnson Street Madison, WI 53706 USA（教育心理学系）
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
Dissecting Failure Dynamics in Large Language Model Reasoning
School of Information Science and Engineering, Yunnan University, Kunming, China（云南大学信息科学与工程学院，昆明，中国）；Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming, China（云南省智能系统与计算重点实验室，昆明，中国）
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Damo Technology Co. Ltd.（北京达摩科技有限公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Hong Kong University of Science and Technology（香港科技大学）
Dissecting AI Trading: Behavioral Finance and Market Bubbles
Saïd Business School, University of Oxford（牛津大学said商学院）；School of Management and Economics, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）管理学院）
From Utterance to Vividity: Training Expressive Subtitle Translation LLM via Adaptive Local Preference Optimization
MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Hujing Digital Media & Entertainment Group（华景数字媒体与娱乐集团）；Geely AI lab（吉利AI实验室）
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
