arXiv AI July 7, 2026 By Stephen Pilli, Vivek Nallur

Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings

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.

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

arXiv Machine Learning

Aug 7

Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

arXiv:2608. 05166v1 Announce Type: cross Abstract: We present an evaluation of cognitive bias expression in state-of-the-art instruction-tuned LLMs under realistic multi-turn interaction settings.

By Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs

llms benchmarks safety

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

5d ago

Unmasking Conversational Bias in AI Multiagent Systems

arXiv:2501. 14844v3 Announce Type: replace-cross Abstract: Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings.

By Erica Coppolillo, Giuseppe Manco, Luca Maria Aiello

llms agents benchmarks safety

arXiv AI

Jun 2

Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents

arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.

By Aitor Arronte Alvarez, Naiyi Xie Fincham

llms agents benchmarks safety

arXiv AI

Jun 18

Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts

arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.

By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys

llms

Related stories

Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

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

Unmasking Conversational Bias in AI Multiagent Systems

Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents

Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts