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Recent advancements in Large Language Models (LLMs) have brought them closer to matching human cognition across a variety of tasks. How well do these models align with human performance in detecting and mapping analogies? Prior research has…

计算与语言 · 计算机科学 2025-07-16 Kalit Inani , Keshav Kabra , Vijay Marupudi , Sashank Varma

As Large Language Models (LLMs) grow in capability, do they develop self-awareness as an emergent behavior? And if so, can we measure it? We introduce the AI Self-Awareness Index (AISAI), a game-theoretic framework for measuring…

人工智能 · 计算机科学 2025-12-04 Kyung-Hoon Kim

As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks…

计算与语言 · 计算机科学 2025-06-10 Yang Xiao , Jiashuo Wang , Qiancheng Xu , Changhe Song , Chunpu Xu , Yi Cheng , Wenjie Li , Pengfei Liu

AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions…

人机交互 · 计算机科学 2024-06-21 Yoonsu Kim , Kihoon Son , Seoyoung Kim , Juho Kim

Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds." Generated agents powered by Large Language…

Large Language Models (LLMs) have shown capabilities close to human performance in various analytical tasks, leading researchers to use them for time and labor-intensive analyses. However, their capability to handle highly specialized and…

计算与语言 · 计算机科学 2024-10-08 Alexander S. Choi , Syeda Sabrina Akter , JP Singh , Antonios Anastasopoulos

Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored. Inspired by Bloom's Taxonomy, we propose a…

计算与语言 · 计算机科学 2025-06-11 Yuxuan Zhou , Xien Liu , Chenwei Yan , Chen Ning , Xiao Zhang , Boxun Li , Xiangling Fu , Shijin Wang , Guoping Hu , Yu Wang , Ji Wu

The ability to represent oneself and others as agents with knowledge, intentions, and belief states that guide their behavior - Theory of Mind - is a human universal that enables us to navigate - and manipulate - the social world. It is…

机器学习 · 计算机科学 2026-05-12 Christopher Ackerman

This paper examines the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you…

Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However, the mechanisms responsible for this success remain opaque,…

计算与语言 · 计算机科学 2024-01-04 Gaël Gendron , Qiming Bao , Michael Witbrock , Gillian Dobbie

Recent evidence suggests Large Language Models (LLMs) display Theory of Mind (ToM) abilities. Most ToM experiments place participants in a spectatorial role, wherein they predict and interpret other agents' behavior. However, human ToM also…

计算与语言 · 计算机科学 2025-07-23 Jared Moore , Ned Cooper , Rasmus Overmark , Beba Cibralic , Nick Haber , Cameron R. Jones

A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively modeled using surprisal from early layers of large language…

计算与语言 · 计算机科学 2026-04-21 Tatsuki Kuribayashi , Alex Warstadt , Yohei Oseki , Ethan Gotlieb Wilcox

Moral competence is the ability to act in accordance with moral principles. As large language models (LLMs) are increasingly deployed in situations demanding moral competence, there is increasing interest in evaluating this ability…

人工智能 · 计算机科学 2026-03-09 Daniel Kilov , Caroline Hendy , Secil Yanik Guyot , Aaron J. Snoswell , Seth Lazar

The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. Inspired by…

计算与语言 · 计算机科学 2024-10-01 Ziwei Ji , Delong Chen , Etsuko Ishii , Samuel Cahyawijaya , Yejin Bang , Bryan Wilie , Pascale Fung

Large language models (LLMs) have revolutionized many areas (e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust and general…

人工智能 · 计算机科学 2024-01-18 Zhiming Li , Yushi Cao , Xiufeng Xu , Junzhe Jiang , Xu Liu , Yon Shin Teo , Shang-wei Lin , Yang Liu

When artificial intelligence mistakes memorization for intelligence, it creates a dangerous mirage of reasoning. Existing studies treat memorization and self-knowledge deficits in LLMs as separate issues and do not recognize an intertwining…

计算与语言 · 计算机科学 2025-12-23 Sahil Kale

The advent of AI driven large language models (LLMs) have stirred discussions about their role in qualitative research. Some view these as tools to enrich human understanding, while others perceive them as threats to the core values of the…

软件工程 · 计算机科学 2023-06-26 Muneera Bano , Didar Zowghi , Jon Whittle

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs…

计算与语言 · 计算机科学 2023-10-10 Thilo Hagendorff , Sarah Fabi , Michal Kosinski

Human beings are considered as the most intelligent species on Earth. The ability to think, to create, to innovate, are the key elements which make humans superior over other existing species on Earth. Machines lack all those elements,…

其他计算机科学 · 计算机科学 2019-07-11 Ravin Kumar