arXiv AI June 17, 2026 By Manon Reusens, Sofie Goethals, Toon Calders, David Martens

Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices

arXiv:2602. 09802v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in applications such as travel assistance and purchasing support, they are often required to make subjective choices on behalf of users in settings where no objectively correct answer exists.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

Hugging Face Trending Papers

Jul 13

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns.

llms agents

arXiv AI

Jul 14

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.

By Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, Zhengbing He

llms agents

arXiv AI

Jun 24

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.

By Yujun Zhou, Christopher M. Ackerman

llms safety

arXiv AI

Jul 28

Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty

arXiv:2508. 08992v4 Announce Type: replace Abstract: Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for modeling human behavior under such uncertainty.

By Rui Wang, Qihan Lin, Jiayu Liu, Qing Zong, Tianshi Zheng, Dadi Guo, Haochen Shi, Peixuan Han, Weiqi Wang, Yangqiu Song

llms safety

arXiv AI

Jun 17

LLM Consumer Behavior Theory: Foundations of a Novel Research Field

arXiv:2606. 18005v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users.

By Manon Reusens, Sofie Goethals, David Martens

llms agents nlp safety

arXiv AI

2d ago

Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest

arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.

By Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, Thomas L. Griffiths

llms reinforcement-learning

Related stories

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty

LLM Consumer Behavior Theory: Foundations of a Novel Research Field

Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest