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Related papers: GeoCode-GPT: A Large Language Model for Geospatial…

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This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and…

Computation and Language · Computer Science 2023-07-10 Yuhan Ji , Song Gao

Multimodal large language models (MLLMs) have shown remarkable capabilities across a broad range of tasks but their knowledge and abilities in the geographic and geospatial domains are yet to be explored, despite potential wide-ranging…

Computer Vision and Pattern Recognition · Computer Science 2024-01-17 Jonathan Roberts , Timo Lüddecke , Rehan Sheikh , Kai Han , Samuel Albanie

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have…

Computer Vision and Pattern Recognition · Computer Science 2026-04-17 Peijie Wang , Ming-Liang Zhang , Jun Cao , Chao Deng , Dekang Ran , Hongda Sun , Pi Bu , Xuan Zhang , Yingyao Wang , Jun Song , Bo Zheng , Fei Yin , Cheng-Lin Liu

Large language models (LLMs), such as ChatGPT and Copilot, are transforming software development by automating code generation and, arguably, enable rapid prototyping, support education, and boost productivity. Therefore, correctness and…

Software Engineering · Computer Science 2024-08-30 Robin Beer , Alexander Feix , Tim Guttzeit , Tamara Muras , Vincent Müller , Maurice Rauscher , Florian Schäffler , Welf Löwe

Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Shihao Cai , Keqin Bao , Hangyu Guo , Jizhi Zhang , Jun Song , Bo Zheng

Automatic code generation has gained significant momentum with the advent of Large Language Models (LLMs) such as GPT-4. Although many studies focus on improving the effectiveness of LLMs for code generation, very limited work tries to…

Software Engineering · Computer Science 2025-06-02 Melika Sepidband , Hamed Taherkhani , Song Wang , Hadi Hemmati

Although Large Language Models (LLMs) have demonstrated remarkable code-generation ability, they still struggle with complex tasks. In real-world software development, humans usually tackle complex tasks through collaborative teamwork, a…

Software Engineering · Computer Science 2024-05-14 Yihong Dong , Xue Jiang , Zhi Jin , Ge Li

Large Language Models (LLMs) are widely adopted for assisting in software development tasks, yet their performance evaluations have narrowly focused on the functional correctness of generated code. Human programmers, however, require…

Software Engineering · Computer Science 2024-12-06 Yun Peng , Akhilesh Deepak Gotmare , Michael Lyu , Caiming Xiong , Silvio Savarese , Doyen Sahoo

Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm for graph problems is hindered by unverifiable steps, limited…

Computation and Language · Computer Science 2024-10-28 Qifan Zhang , Xiaobin Hong , Jianheng Tang , Nuo Chen , Yuhan Li , Wenzhong Li , Jing Tang , Jia Li

Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum of open-domain knowledge. Besides, their…

Software Engineering · Computer Science 2025-02-18 Xiaodong Gu , Meng Chen , Yalan Lin , Yuhan Hu , Hongyu Zhang , Chengcheng Wan , Zhao Wei , Yong Xu , Juhong Wang

Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited--a critical requirement in real-world software systems. We argue that current…

Software Engineering · Computer Science 2026-05-11 Jiuding Yang , Shengyao Lu , Hongxuan Liu , Shayan Shirahmad Gale Bagi , Zahra Fazel , Tomasz Czajkowski , Di Niu

The widespread adoption of large language models (LLMs), such as OpenAI's ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate plausible-sounding but false outputs,…

Computation and Language · Computer Science 2023-06-22 Krishna Kumar

Large Language Models (LLMs) have advanced rapidly as tools for automating code generation in scientific research, yet their ability to interpret and use unfamiliar Python APIs for complex computational experiments remains poorly…

Software Engineering · Computer Science 2025-09-17 Nuno Fachada , Daniel Fernandes , Carlos M. Fernandes , Bruno D. Ferreira-Saraiva , João P. Matos-Carvalho

Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance the reasoning capabilities of remote sensing MLLMs, typically through cold-start…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Di Wang , Shunyu Liu , Wentao Jiang , Fengxiang Wang , Yi Liu , Xiaolei Qin , Zhiming Luo , Chaoyang Zhou , Haonan Guo , Jing Zhang , Bo Du , Dacheng Tao , Liangpei Zhang

Large Language Models (LLMs) have shown promising potentials in program generation and no-code automation. However, LLMs are prone to generate hallucinations, i.e., they generate text which sounds plausible but is incorrect. Although there…

Software Engineering · Computer Science 2025-07-10 Vibhor Agarwal , Yulong Pei , Salwa Alamir , Xiaomo Liu

Large Language Models (LLMs) have shown impressive capabilities across a wide variety of tasks. However, they still face challenges with long-horizon planning. To study this, we propose path planning tasks as a platform to evaluate LLMs'…

Artificial Intelligence · Computer Science 2024-06-24 Mohamed Aghzal , Erion Plaku , Ziyu Yao

Large Language Models have shown prominent capabilities in generating functional code from natural language descriptions. However, a standardized way to evaluate these capabilities in an objective and unbiased manner is still to be found.…

Software Engineering · Computer Science 2024-10-23 Álvaro Barbero Jiménez

Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in high-performance computing (HPC) domain tasks has…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-11-23 Xianzhong Ding , Le Chen , Murali Emani , Chunhua Liao , Pei-Hung Lin , Tristan Vanderbruggen , Zhen Xie , Alberto E. Cerpa , Wan Du

Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications in this field. To…

Computation and Language · Computer Science 2023-09-15 Cheng Deng , Tianhang Zhang , Zhongmou He , Yi Xu , Qiyuan Chen , Yuanyuan Shi , Luoyi Fu , Weinan Zhang , Xinbing Wang , Chenghu Zhou , Zhouhan Lin , Junxian He

The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision. This paper explores the capabilities of GPT-4V in the realms of geography, environmental…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Chenjiao Tan , Qian Cao , Yiwei Li , Jielu Zhang , Xiao Yang , Huaqin Zhao , Zihao Wu , Zhengliang Liu , Hao Yang , Nemin Wu , Tao Tang , Xinyue Ye , Lilong Chai , Ninghao Liu , Changying Li , Lan Mu , Tianming Liu , Gengchen Mai