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
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.
The paper introduces a prompt-response concept model that links the amount of task-relevant information in a prompt to the uncertainty of responses generated by large language models (LLMs). It identifies four sources of response uncertainty—prompt underspecification, model quality, task variability, and semantic redundancy—and demonstrates that uncertainty decreases as prompt informativeness or model quality increases, analogous to epistemic uncertainty in probabilistic models. Experiments on real-world datasets confirm the theoretical predictions and validate the model.
By Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low
arXiv:2609.07943v1 Announce Type: new
Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...
By Alex Smolin, Bryan Wilder
The paper examines how large language models (LLMs) express uncertainty compared to humans, noting that humans use verbal markers like "possible" or "likely" to convey metacognitive awareness. By curating a corpus of human uncertainty markers and benchmarking LLMs against it, the authors find that LLMs encode these markers with numerical levels that differ substantially from human usage. They introduce METHODNAME, an optimization-based algorithm that learns an optimal uncertainty profile over verbal markers directly from LLM outputs, enabling a direct comparison of confidence semantics and revealing systematic disparities in verbal expressions.
By Jinhao Duan, Zicheng Liu, Zijie Liu, Kaidi Xu, Tianlong Chen
The paper introduces a decision‑theoretic framework that elicits both probability judgments and decisions from large language models (LLMs) to test whether their reported beliefs are consistent with their actions. It shows that this framework yields empirically testable conditions without assuming a specific utility function. In clinical diagnosis simulations, the authors find that while LLMs’ reported beliefs are not perfect reflections of the information in their decisions, the discrepancies are small for the strongest models.
By Khurram Yamin, Jingjing Tang, Santiago Cortes-Gomez, Amit Sharma, Eric Horvitz, Bryan Wilder