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相关论文: LLMs4Life: Large Language Models for Ontology Lear…

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Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural…

机器学习 · 计算机科学 2024-12-05 Minghao Shao , Abdul Basit , Ramesh Karri , Muhammad Shafique

Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critical shortcomings: (i) the reliance on simplified use cases,…

Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension…

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

Background: Over the past few decades, the process and methodology of automated question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the…

人工智能 · 计算机科学 2024-12-06 Dominic Lohr , Marc Berges , Abhishek Chugh , Michael Kohlhase , Dennis Müller

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with both human language, symbols, code, and numerical data. Here…

计算与语言 · 计算机科学 2023-11-01 Markus J. Buehler

Service robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontologies face a challenge because common-sense knowledge is often…

机器人学 · 计算机科学 2023-11-16 Felix Ocker , Jörg Deigmöller , Julian Eggert

Schemas play a vital role in ensuring data quality and supporting usability in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts.…

人工智能 · 计算机科学 2025-10-03 Bohui Zhang , Yuan He , Lydia Pintscher , Albert Meroño Peñuela , Elena Simperl

Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant…

分布式、并行与集群计算 · 计算机科学 2025-02-25 Yue Zheng , Yuhao Chen , Bin Qian , Xiufang Shi , Yuanchao Shu , Jiming Chen

Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving capabilities of LLMs in the domain of thermodynamics. A…

计算工程、金融与科学 · 计算机科学 2025-12-18 Rebecca Loubet , Pascal Zittlau , Luisa Vollmer , Marco Hoffmann , Sophie Fellenz , Fabian Jirasek , Heike Leitte , Hans Hasse

Large language models (LLMs) have ushered in a new era for processing complex information in various fields, including science. The increasing amount of scientific literature allows these models to acquire and understand scientific…

计算与语言 · 计算机科学 2024-08-21 Huy Quoc To , Ming Liu , Guangyan Huang

This paper outlines the LLMs4OL 2024, the first edition of the Large Language Models for Ontology Learning Challenge. LLMs4OL is a community development initiative collocated with the 23rd International Semantic Web Conference (ISWC) to…

计算与语言 · 计算机科学 2024-09-17 Hamed Babaei Giglou , Jennifer D'Souza , Sören Auer

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve…

Large Language Models (LLMs) have shown remarkable capabilities across various domains, yet they struggle with knowledge-intensive tasks in areas that demand factual accuracy, e.g. industrial automation and healthcare. Key limitations…

机器学习 · 计算机科学 2025-09-10 Michael Banf , Johannes Kuhn

Large language models (LLMs) are powerful artificial intelligence (AI) tools transforming how research is conducted. However, their use in research has been met with skepticism, due to concerns about hallucinations, biases and potential…

人工智能 · 计算机科学 2025-07-08 Ruian Ke , Ruy M. Ribeiro

Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies…

人工智能 · 计算机科学 2025-04-08 Jiaoyan Chen , Olga Mashkova , Fernando Zhapa-Camacho , Robert Hoehndorf , Yuan He , Ian Horrocks

Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art…

网络与互联网体系结构 · 计算机科学 2024-06-26 Danshi Wang , Yidi Wang , Xiaotian Jiang , Yao Zhang , Yue Pang , Min Zhang

Recent advancements in Large Language Models (LLMs) have sparked interest in their potential applications across various fields. This paper embarked on a pivotal inquiry: Can existing LLMs effectively serve as "water expert models" for…

计算与语言 · 计算机科学 2024-08-01 Boyan Xu , Liang Wen , Zihao Li , Yuxing Yang , Guanlan Wu , Xiongpeng Tang , Yu Li , Zihao Wu , Qingxian Su , Xueqing Shi , Yue Yang , Rui Tong , How Yong Ng

Although Large Language Models (LLMs) perform exceptionally well in general domains, the problem of hallucinations poses significant risks in specialized fields such as healthcare and law, where high interpretability is essential. Existing…

计算与语言 · 计算机科学 2026-03-18 Mingchen Tu , Zhiqiang Liu , Juan Li , Liangyurui Liu , Junjie Wang , Lei Liang , Wen Zhang