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Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam…

计算与语言 · 计算机科学 2025-03-17 Dibyanayan Bandyopadhyay , Soham Bhattacharjee , Asif Ekbal

We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and…

机器人学 · 计算机科学 2024-09-30 Philipp Allgeuer , Hassan Ali , Stefan Wermter

As Large Language Models (LLMs) are increasingly deployed in customer-facing applications, a critical yet underexplored question is how users communicate differently with LLM chatbots compared to human agent. In this study, we present…

计算与语言 · 计算机科学 2025-10-06 Fulei Zhang , Zhou Yu

Modern Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction. The current generation of Large Language Models (LLMs) have been linked to claims about human-like linguistic…

计算与语言 · 计算机科学 2024-09-05 Evelina Leivada , Gary Marcus , Fritz Günther , Elliot Murphy

Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through natural conversation. However, most systems remain constrained by a linear request-response…

计算与语言 · 计算机科学 2026-05-05 Jiaqi Chen , Yanzhe Zhang , Yutong Zhang , Yijia Shao , Diyi Yang

As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grounded in science communication research, to assess LLM…

This paper investigates the potential of AI models, particularly large language models (LLMs), to support knowledge exploration and augment human creativity during ideation. We present "Latent Lab" an interactive tool for discovering…

人工智能 · 计算机科学 2023-11-23 Kevin Dunnell , Trudy Painter , Andrew Stoddard , Andy Lippman

Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM capacities have taken off, but reflection is lagging behind. Thus, in this…

计算与语言 · 计算机科学 2023-11-01 Bram M. A. van Dijk , Tom Kouwenhoven , Marco R. Spruit , Max J. van Duijn

This paper explores the potential of large language models (LLMs) as reliable analytical tools in linguistic research, focusing on the emergence of affective meanings in temporal expressions involving manner-of-motion verbs. While LLMs like…

计算与语言 · 计算机科学 2025-07-15 Rosa Illan Castillo , Javier Valenzuela

The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code generation, to composing essays, to passing STEM and legal…

人工智能 · 计算机科学 2025-02-28 Konstantina Christakopoulou , Iris Qu , John Canny , Andrew Goodridge , Cj Adams , Minmin Chen , Maja Matarić

Eye-tracking data reveals valuable insights into users' cognitive states but is difficult to analyze due to its structured, non-linguistic nature. While large language models (LLMs) excel at reasoning over text, they struggle with temporal…

人机交互 · 计算机科学 2025-07-25 Dongyang Guo , Yasmeen Abdrabou , Enkeleda Thaqi , Enkelejda Kasneci

As Large Language Models (LLMs) and other forms of Generative AI permeate various aspects of our lives, their application for learning and education has provided opportunities and challenges. This paper presents an investigation into the…

计算机与社会 · 计算机科学 2023-06-06 Daniel Leiker , Sara Finnigan , Ashley Ricker Gyllen , Mutlu Cukurova

While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs). These…

计算与语言 · 计算机科学 2024-08-23 Kian Ahrabian , Zhivar Sourati , Kexuan Sun , Jiarui Zhang , Yifan Jiang , Fred Morstatter , Jay Pujara

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

Understanding the behavior of large language models (LLMs) is crucial for ensuring their safe and reliable use. However, existing explainable AI (XAI) methods for LLMs primarily rely on word-level explanations, which are often…

Simulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations presents significant challenges, particularly regarding their…

计算机与社会 · 计算机科学 2025-02-26 Qian Wang , Zhenheng Tang , Bingsheng He

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively unexplored. In this paper, we conduct a series of…

机器学习 · 计算机科学 2023-10-10 Yuntong Hu , Zheng Zhang , Liang Zhao

A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like…

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the…

Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing…

计算与语言 · 计算机科学 2024-02-16 Min Zhang , Sato Takumi , Jack Zhang , Jun Wang