Human-centered assessment tasks, which are essential for systematic decision-making, rely heavily on human judgment and typically lack verifiable ground truth. Existing approaches face a dilemma: methods using only human judgments suffer from heterogeneous expertise and inconsistent rating scales, while methods using only model-generated scores must learn from imperfect proxies or incomplete features.
arXiv:2608. 02455v1 Announce Type: new Abstract: Human-centered assessment tasks, which are essential for systematic decision-making, rely heavily on human judgment and typically lack verifiable ground truth.
By Zejun Xie, Xintong Li, Guang Wang, Desheng Zhang
arXiv:2607. 16239v1 Announce Type: new Abstract: AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges, tasks, and domains.
By Lei Shi, Anlan Zhang, Rita Lyu, Zhengmian Hu, Tong Yu, David Arbour, Avi Feller, Saayan Mitra, Ritwik Sinha
arXiv:2608.30842v1 Announce Type: new
Abstract: Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often...
By Deepak Pandita, Christopher M. Homan
arXiv:2605. 13801v2 Announce Type: replace-cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount.
By Deepak Pandita, Flip Korn, Chris Welty, Christopher M. Homan
arXiv:2601. 21817v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.
By Mingyuan Xu, Xinzi Tan, Jiawei Wu, Doudou Zhou