Large Language Models (LLMs) are increasingly used as scalable evaluators of model outputs, but their preference judgments exhibit systematic biases and can diverge from human evaluations. Prior work on LLM-as-a-judge has largely focused on a small, predefined set of hypothesized biases, leaving open the problem of automatically discovering unknown drivers of LLM preferences. We address this gap by studying several embedding-level concept extraction methods for analyzing LLM judge behavior. We compare these methods in terms of interpretability and predictiveness, finding that sparse autoencoder-based approaches recover substantially more interpretable preference features than alternatives while remaining competitive in predicting LLM decisions. Using over 27k paired responses from multiple human preference datasets and judgments from three LLMs, we analyze LLM judgments and compare them to those of human annotators. Our method both validates existing results, such as the tendency for LLMs to prefer refusal of sensitive requests at higher rates than humans, and uncovers new trends across both general and domain-specific datasets, including biases toward responses that emphasize concreteness and empathy in approaching new situations, toward detail and formality in academic advice, and against legal guidance that promotes active steps like calling police and filing lawsuits. Our results show that automated concept discovery enables systematic analysis of LLM judge preferences without predefined bias taxonomies.
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Arizona State University（亚利桑那州立大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Notre Dame（诺丁汉大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）；Yale University（耶鲁大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
BiasScope: Towards Automated Detection of Bias in LLM-as-a-Judge Evaluation
Southern University of Science and Technology（南方科技大学）；Alibaba Group（阿里巴巴集团）；Beihang University（北航）；Shanghai University of Finance and Economics（上海财经大学）
Quantifying and Mitigating Self-Preference Bias of LLM Judges
CompleX Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China（复杂实验室、计算机科学与工程学院、电子科技大学、成都、中国）
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）
