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The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they support the same languages as models developed in the United States…

计算与语言 · 计算机科学 2026-05-18 Andrea W Wen-Yi , Unso Eun Seo Jo , David Mimno

Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text outputs. However, they falter when requests to generate…

计算机视觉与模式识别 · 计算机科学 2023-09-15 Xinyu Wang , Bohan Zhuang , Qi Wu

Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life…

人工智能 · 计算机科学 2025-06-03 Jie Feng , Tianhui Liu , Yuwei Du , Siqi Guo , Yuming Lin , Yong Li

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and…

Large language models (LLMs) have received considerable attention recently due to their outstanding comprehension and reasoning capabilities, leading to great progress in many fields. The advancement of LLM techniques also offers promising…

系统与控制 · 电气工程与系统科学 2024-09-17 Hao Zhou , Chengming Hu , Ye Yuan , Yufei Cui , Yili Jin , Can Chen , Haolun Wu , Dun Yuan , Li Jiang , Di Wu , Xue Liu , Charlie Zhang , Xianbin Wang , Jiangchuan Liu

Large Language Models (LLMs) are increasingly applied in the fields of mechanical engineering and materials science. As models that establish connections through the interface of language, LLMs can be applied for step-wise reasoning through…

应用物理 · 物理学 2025-07-22 Adrian Ehrenhofer , Thomas Wallmersperger , Gianaurelio Cuniberti

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error…

计算与语言 · 计算机科学 2023-08-21 Yaxin Fan , Feng Jiang , Peifeng Li , Haizhou Li

Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as superior in handling various natural language processing tasks…

计算与语言 · 计算机科学 2023-11-30 Han Cao , Lingwei Wei , Mengyang Chen , Wei Zhou , Songlin Hu

The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently. However, their evaluation in the benchmark academic datasets remains under-explored due to the difficulty of evaluating the generative…

The rapid development of large language models (LLMs) is reshaping operational paradigms across multidisciplinary domains. LLMs' emergent capability to synthesize policy-relevant insights across disciplinary boundaries suggests potential as…

计算机与社会 · 计算机科学 2025-04-22 Jinghan Ke , Zheng Zhou , Yuxuan Zhao

Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression. However, medical environments in…

Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn to understand and generate language by training billions of…

计算与语言 · 计算机科学 2024-08-29 Wazir Ali , Sampo Pyysalo

No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowledge with a rich history. To bridge this gap, we present a…

计算与语言 · 计算机科学 2024-03-15 Li Yizhen , Huang Shaohan , Qi Jiaxing , Quan Lei , Han Dongran , Luan Zhongzhi

Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face…

机器学习 · 计算机科学 2025-04-21 Sihang Li , Jin Huang , Jiaxi Zhuang , Yaorui Shi , Xiaochen Cai , Mingjun Xu , Xiang Wang , Linfeng Zhang , Guolin Ke , Hengxing Cai

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…

This paper introduces an approach that combines the language reasoning capabilities of large language models (LLMs) with the benefits of local training to tackle complex, domain-specific tasks. Specifically, the authors demonstrate their…

计算与语言 · 计算机科学 2023-08-04 V. K. Cody Bumgardner , Aaron Mullen , Sam Armstrong , Caylin Hickey , Jeff Talbert

Large Language Models (LLMs), such as Generative Pre-trained Transformers (GPTs) are revolutionizing the generation of human-like text, producing contextually relevant and syntactically correct content. Despite challenges like biases and…

计算与语言 · 计算机科学 2025-08-04 Alper Yaman , Jannik Schwab , Christof Nitsche , Abhirup Sinha , Marco Huber

Adapting general large language models (LLMs) to specialized domains presents great challenges due to varied data distributions. This adaptation typically requires continual pre-training on massive domain-specific corpora to facilitate…

计算与语言 · 计算机科学 2024-07-16 Jinhao Jiang , Junyi Li , Wayne Xin Zhao , Yang Song , Tao Zhang , Ji-Rong Wen

Large Language Models (LLMs) have shown remarkable success in supporting a wide range of knowledge-intensive tasks. In specialized domains, there is growing interest in leveraging LLMs to assist subject matter experts with domain-specific…

The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy,…

计算与语言 · 计算机科学 2023-10-24 Liyizhe Peng , Zixing Zhang , Tao Pang , Jing Han , Huan Zhao , Hao Chen , Björn W. Schuller