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相关论文: Can the capability of Large Language Models be des…

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Do large language models (LLMs) display rational reasoning? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rational reasoning remains less clear. In this paper, we answer…

计算与语言 · 计算机科学 2024-02-16 Olivia Macmillan-Scott , Mirco Musolesi

While large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks by acquiring rich factual knowledge from their broad training data, their ability to synthesize and logically…

计算与语言 · 计算机科学 2024-07-31 Tianshi Zheng , Jiaxin Bai , Yicheng Wang , Tianqing Fang , Yue Guo , Yauwai Yim , Yangqiu Song

Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset…

计算与语言 · 计算机科学 2024-04-23 Carlos Carrasco-Farre

To what extent do LLMs use their capabilities towards their given goal? We take this as a measure of their goal-directedness. We evaluate goal-directedness on tasks that require information gathering, cognitive effort, and plan execution,…

Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) have shown promise in replicating…

计算与语言 · 计算机科学 2025-06-23 Ziyan Cui , Ning Li , Huaikang Zhou

Human communication is often implicit, conveying tone, identity, and intent beyond literal meanings. While large language models have achieved strong performance on explicit tasks such as summarization and reasoning, their capacity for…

计算与语言 · 计算机科学 2026-02-09 Joshua Tint , Som Sagar , Aditya Taparia , Kelly Raines , Bimsara Pathiraja , Caleb Liu , Ransalu Senanayake

Word-level psycholinguistic norms lend empirical support to theories of language processing. However, obtaining such human-based measures is not always feasible or straightforward. One promising approach is to augment human norming datasets…

Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt…

人工智能 · 计算机科学 2024-06-18 Ming Cheung

Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural language on a range of tasks. As LLMs increasingly influence…

Have Large Language Models (LLMs) developed a personality? The short answer is a resounding "We Don't Know!". In this paper, we show that we do not yet have the right tools to measure personality in language models. Personality is an…

计算与语言 · 计算机科学 2023-05-25 Xiaoyang Song , Akshat Gupta , Kiyan Mohebbizadeh , Shujie Hu , Anant Singh

We assess how the code reasoning abilities of large language models (LLMs) generalize to different kinds of programs. We present techniques for obtaining in- and out-of-distribution programs with different characteristics: code sampled from…

软件工程 · 计算机科学 2025-04-09 Rem Yang , Julian Dai , Nikos Vasilakis , Martin Rinard

The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conjecturing a positive 'quality-power' relationship (Wilcox et al., 2023), in which language…

计算与语言 · 计算机科学 2025-05-20 Yi-Chien Lin , Hongao Zhu , William Schuler

The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology. Scholars have been studying the inherent personalities exhibited by LLMs and attempting to…

人工智能 · 计算机科学 2025-03-25 Lucio La Cava , Andrea Tagarelli

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge…

The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infer others' beliefs, intentions, and emotions from text. Given that LLMs are trained on language…

计算与语言 · 计算机科学 2026-03-20 Anna Babarczy , Andras Lukacs , Peter Vedres , Zeteny Bujka

Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making…

计算与语言 · 计算机科学 2024-03-01 Chenglei Shen , Guofu Xie , Xiao Zhang , Jun Xu

Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Consequently, recent studies have suggested various unique,…

计算与语言 · 计算机科学 2024-01-23 Chen Zhang , Luis Fernando D'Haro , Yiming Chen , Malu Zhang , Haizhou Li

Large Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to prevent them from answering illegal questions, imbalances in…

计算与语言 · 计算机科学 2025-03-18 Likai Tang , Niruth Bogahawatta , Yasod Ginige , Jiarui Xu , Shixuan Sun , Surangika Ranathunga , Suranga Seneviratne

Recently, Large Language Models (LLM) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet.…

计算与语言 · 计算机科学 2024-02-21 Israt Jahan , Md Tahmid Rahman Laskar , Chun Peng , Jimmy Huang

Large language models generate judgments that resemble those of humans. Yet the extent to which these models align with human judgments in interpreting figurative and socially grounded language remains uncertain. To investigate this, human…

计算与语言 · 计算机科学 2026-01-15 Samhita Bollepally , Aurora Sloman-Moll , Takashi Yamauchi