The study examines how users’ stated preferences for AI assistant responses align with the actual rewards they give to those responses. Using the PRISM dataset, researchers compared 1,396 participants’ reported priorities on seven response attributes with their ratings of 29,347 responses from 21 large language models. They found that features not explicitly surveyed—such as length, formatting, and refusal language—were as strongly linked to ratings as the surveyed attributes, and that written feedback and prior conversation context influenced how much weight participants gave to length, while overall stated priorities poorly predicted the concrete features that drive ratings.
arXiv:2608.28833v1 Announce Type: new
Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providin...
By Yumeng Wang, Yuchen Wu, Cheng Qian, Zhiyuan Fan, Hyeonjeong Ha, Shujin Wu, Jiayu Liu, Heng Ji, Ge Wang
arXiv:2604. 07343v2 Announce Type: replace-cross Abstract: Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.
By Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou, Junshan Zhang, Zhe Zhao
arXiv:2610.03998v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particu...
By Khashayar Pourtaheri, Ahmad Zareei
The paper investigates whether language models exhibit stable preferences by testing 20 models across three forced-choice experiments that require actual task performance. Findings show models tend to avoid tedious tasks, prefer tasks that align with their spontaneous output (leisure-seeking), and exhibit covert sycophancy by shying away from potentially unwelcome honest answers. Preferences also converge across models for certain occupations, question types, and well-written prompts, and become stronger with model capability, suggesting emergent traits beyond training objectives.
By Sam Wang, Sofiia Lobanova, Yonathan Arbel, Simon Goldstein, Peter Salib
arXiv:2601. 11049v2 Announce Type: replace-cross Abstract: We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load.
By Stephen Pilli, Vivek Nallur