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In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields. However, the…

Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their performance may be suboptimal for domain-specific tasks that…

计算与语言 · 计算机科学 2023-05-19 Ziyang Luo , Can Xu , Pu Zhao , Xiubo Geng , Chongyang Tao , Jing Ma , Qingwei Lin , Daxin Jiang

Large Language Models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational…

Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or…

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on…

计算与语言 · 计算机科学 2023-04-13 Harsha Nori , Nicholas King , Scott Mayer McKinney , Dean Carignan , Eric Horvitz

Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and…

计算与语言 · 计算机科学 2025-03-25 Shervin Minaee , Tomas Mikolov , Narjes Nikzad , Meysam Chenaghlu , Richard Socher , Xavier Amatriain , Jianfeng Gao

Generative Large Language Models (gLLMs), such as ChatGPT, are increasingly being used in communication research for content analysis. Studies show that gLLMs can outperform both crowd workers and trained coders, such as research…

人工智能 · 计算机科学 2025-10-29 Daria Kravets-Meinke , Hannah Schmid-Petri , Sonja Niemann , Ute Schmid

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability (e.g., reasoning). While LLMs…

计算与语言 · 计算机科学 2024-11-22 Bowen Jin , Gang Liu , Chi Han , Meng Jiang , Heng Ji , Jiawei Han

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including…

Large language models (LLMs) constitute a breakthrough state-of-the-art Artificial Intelligence technology which is rapidly evolving and promises to aid in medical diagnosis. However, the correctness and the accuracy of their returns has…

计算与语言 · 计算机科学 2024-02-07 Dimitrios P. Panagoulias , Maria Virvou , George A. Tsihrintzis

Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses…

Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending…

计算工程、金融与科学 · 计算机科学 2025-01-24 Chao Wang , Hehe Fan , Ruijie Quan , Yi Yang

Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code. Like GP, it uses…

神经与进化计算 · 计算机科学 2024-01-17 Erik Hemberg , Stephen Moskal , Una-May O'Reilly

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for…

In recent years, large language models (LLMs) have achieved state-of-the-art results in various biological sequence analysis tasks, such as sequence classification, structure prediction, and function prediction. Similar to advancements in…

基因组学 · 定量生物学 2025-02-19 Wang Liang

Although traditional statistical techniques and machine learning methods have contributed significantly to genetics and, in particular, inherited disease diagnosis, they often struggle with complex, high-dimensional data, a challenge now…

机器学习 · 计算机科学 2025-08-29 Anton Changalidis , Yury Barbitoff , Yulia Nasykhova , Andrey Glotov

The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in…

定量方法 · 定量生物学 2024-07-09 Yiqing Shen , Zan Chen , Michail Mamalakis , Luhan He , Haiyang Xia , Tianbin Li , Yanzhou Su , Junjun He , Yu Guang Wang

The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare. Specifically, the application of LLMs in patient-trial matching, which involves assessing patient eligibility…

人工智能 · 计算机科学 2023-12-18 Mauro Nievas , Aditya Basu , Yanshan Wang , Hrituraj Singh

In the past year, there has been a growing trend in applying Large Language Models (LLMs) to the field of medicine, particularly with the advent of advanced language models such as ChatGPT developed by OpenAI. However, there is limited…

计算与语言 · 计算机科学 2024-02-27 Fujian Jia , Xin Liu , Lixi Deng , Jiwen Gu , Chunchao Pu , Tunan Bai , Mengjiang Huang , Yuanzhi Lu , Kang Liu

Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph generation remain…

机器学习 · 计算机科学 2024-03-22 Yang Yao , Xin Wang , Zeyang Zhang , Yijian Qin , Ziwei Zhang , Xu Chu , Yuekui Yang , Wenwu Zhu , Hong Mei