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Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning abilities therefore holds significant importance. We argue…

A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interactive question-and-answer benchmarks. However, theoretical work in the field suggests that…

计算与语言 · 计算机科学 2026-02-20 Jared Moore , Rasmus Overmark , Ned Cooper , Beba Cibralic , Nick Haber , Cameron R. Jones

As large language models (LLMs) are increasingly used for work, personal, and therapeutic purposes, researchers have begun to investigate these models' implicit and explicit moral views. Previous work, however, focuses on asking LLMs to…

计算机与社会 · 计算机科学 2025-03-25 Andrew J. Peterson

Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary…

计算机与社会 · 计算机科学 2026-04-07 Zhicheng Lin

Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended…

计算与语言 · 计算机科学 2024-12-24 Cameron R. Jones , Benjamin K. Bergen

Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing…

人工智能 · 计算机科学 2025-11-12 Alejandro R. Jadad

Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently,…

计算与语言 · 计算机科学 2024-04-22 Taicheng Guo , Xiuying Chen , Yaqi Wang , Ruidi Chang , Shichao Pei , Nitesh V. Chawla , Olaf Wiest , Xiangliang Zhang

Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their…

人机交互 · 计算机科学 2026-01-21 Jingshu Li , Tianqi Song , Nattapat Boonprakong , Zicheng Zhu , Yitian Yang , Yi-Chieh Lee

Large language model-based agents are becoming increasingly popular as a low-cost mechanism to provide personalized, conversational advice, and have demonstrated impressive capabilities in relatively simple scenarios, such as movie…

人工智能 · 计算机科学 2025-04-16 Takehiro Takayanagi , Kiyoshi Izumi , Javier Sanz-Cruzado , Richard McCreadie , Iadh Ounis

Prior research has established associations between individuals' language usage and their personal traits; our linguistic patterns reveal information about our personalities, emotional states, and beliefs. However, with the increasing…

计算与语言 · 计算机科学 2024-04-04 Zhivar Sourati , Meltem Ozcan , Colin McDaniel , Alireza Ziabari , Nuan Wen , Ala Tak , Fred Morstatter , Morteza Dehghani

Large Language Models (LLMs) are widely used as conversational agents, exploiting their capabilities in various sectors such as education, law, medicine, and more. However, LLMs are often subjected to context-shifting behaviour, resulting…

计算与语言 · 计算机科学 2025-02-18 Pranav Bhandari , Nicolas Fay , Michael Wise , Amitava Datta , Stephanie Meek , Usman Naseem , Mehwish Nasim

This report analyzes the potential for large language models (LLMs) to expedite accurate replication and generalization of published research about message effects in marketing. LLM-powered participants (personas) were tested by replicating…

计算与语言 · 计算机科学 2025-04-28 Leo Yeykelis , Kaavya Pichai , James J. Cummings , Byron Reeves

Large language models (LLMs) are increasingly being used in conversational roles, yet little is known about how intimacy emerges in human-LLM interactions. Although previous work emphasized the importance of self-disclosure in human-chatbot…

人机交互 · 计算机科学 2025-06-02 Yeseon Hong , Junhyuk Choi , Minju Kim , Bugeun Kim

Deploying large language models (LLMs) with agency in real-world applications raises critical questions about how these models will behave. In particular, how will their decisions align with humans when faced with moral dilemmas? This study…

计算机与社会 · 计算机科学 2025-04-16 Jiseon Kim , Jea Kwon , Luiz Felipe Vecchietti , Alice Oh , Meeyoung Cha

Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choices. Prior evaluations frequently conflate base-model…

人工智能 · 计算机科学 2026-02-03 Xuan Liu , Haoyang Shang , Zizhang Liu , Xinyan Liu , Yunze Xiao , Yiwen Tu , Haojian Jin

Large Language Models have shown exceptional generative abilities in various natural language and generation tasks. However, possible anthropomorphization and leniency towards failure cases have propelled discussions on emergent abilities…

机器人学 · 计算机科学 2024-01-18 Mudit Verma , Siddhant Bhambri , Subbarao Kambhampati

The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs…

计算机与社会 · 计算机科学 2025-05-23 Muhammad Shahrul Zaim bin Ahmad , Kazuhiro Takemoto

Recent developments in large language models (LLMs) have unlocked new opportunities for healthcare, from information synthesis to clinical decision support. These new LLMs are not just capable of modeling language, but can also act as…

Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) generate responses that are often judged more empathic than human-written ones. Yet when a…

计算与语言 · 计算机科学 2026-03-17 Aakriti Kumar , Nalin Poungpeth , Diyi Yang , Bruce Lambert , Matthew Groh

We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from…

计算与语言 · 计算机科学 2026-01-01 Casey O. Barkan , Sid Black , Oliver Sourbut
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