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In the rapidly evolving field of Large Language Models (LLMs), there is a critical need to thoroughly analyze their capabilities and risks. Central to our investigation are two novel elements. Firstly, it is the innovative parallels between…

人工智能 · 计算机科学 2024-03-07 Jianqiiu Zhang

Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing. However, their large-scale deployment remains constrained by the need for significant computational resources.…

计算与语言 · 计算机科学 2025-08-07 Julián Camilo Velandia Gutiérrez

This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have…

人工智能 · 计算机科学 2024-11-27 Abdullah Al Rabeyah , Fabrício Góes , Marco Volpe , Talles Medeiros

Large language models (LLMs) have achieved remarkable progress in code generation, yet their true programming competence remains underexplored. We introduce the Code Triangle framework, which systematically evaluates LLMs across three…

计算与语言 · 计算机科学 2025-07-09 Taolin Zhang , Zihan Ma , Maosong Cao , Junnan Liu , Songyang Zhang , Kai Chen

Large language models (LLMs) have been widely applied to assist in finding solutions for diverse questions. Prior work has proposed representing a method as a pair of a question and its corresponding solution, enabling method reuse.…

计算与语言 · 计算机科学 2025-10-17 Hong Su

Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic solvers to improve logical…

计算与语言 · 计算机科学 2023-10-20 Liangming Pan , Alon Albalak , Xinyi Wang , William Yang Wang

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal…

计算与语言 · 计算机科学 2024-09-12 David Ilić , Gilles E. Gignac

Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their…

人工智能 · 计算机科学 2025-11-21 Parshin Shojaee , Iman Mirzadeh , Keivan Alizadeh , Maxwell Horton , Samy Bengio , Mehrdad Farajtabar

Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate…

软件工程 · 计算机科学 2025-04-03 Nam Huynh , Beiyu Lin

Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view…

人工智能 · 计算机科学 2026-04-01 Chao Wu , Baoheng Li , Mingchen Gao , Yu Tian , Zhenyi Wang

Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are trained on what humans write and are thus exposed to human…

软件工程 · 计算机科学 2025-10-14 Fengfei Sun , Ningke Li , Kailong Wang , Lorenz Goette

The rapid evolution of Large Language Models (LLMs) has markedly expanded their application across diverse domains, transforming how complex problems are approached and solved. Initially conceived to predict subsequent words in texts, these…

人工智能 · 计算机科学 2024-07-11 Sumedh Rasal , E. J. Hauer

Existing reasoning evaluation frameworks for Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) predominantly assess either text-based reasoning or vision-language understanding capabilities, with limited dynamic…

计算与语言 · 计算机科学 2025-08-13 Jixuan Leng , Chengsong Huang , Langlin Huang , Bill Yuchen Lin , William W. Cohen , Haohan Wang , Jiaxin Huang

Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) continue to struggle to generate analogies of comparable quality to those produced by humans.…

计算与语言 · 计算机科学 2026-05-26 Mariam Barakat , Ekaterina Kochmar

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large…

The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize,…

Large Language Models (LLMs) excel at many tasks, yet they struggle to produce truly creative, diverse ideas. In this paper, we introduce a novel approach that enhances LLM creativity. We apply LLMs for translating between natural language…

计算与语言 · 计算机科学 2025-09-30 Moran Mizrahi , Chen Shani , Gabriel Stanovsky , Dan Jurafsky , Dafna Shahaf

Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in…

计算与语言 · 计算机科学 2024-06-24 Yue Huang , Chenrui Fan , Yuan Li , Siyuan Wu , Tianyi Zhou , Xiangliang Zhang , Lichao Sun

Metaphors are everywhere. They appear extensively across all domains of natural language, from the most sophisticated poetry to seemingly dry academic prose. A significant body of research in the cognitive science of language argues for the…

计算与语言 · 计算机科学 2024-10-14 Rebecca M. M. Hicke , Ross Deans Kristensen-McLachlan

Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-like levels. However, their generalisation and robustness when…

计算与语言 · 计算机科学 2025-01-20 Qiming Bao , Gael Gendron , Alex Yuxuan Peng , Wanjun Zhong , Neset Tan , Yang Chen , Michael Witbrock , Jiamou Liu