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Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, this survey probes the core challenges that the rise of LLMs…

Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across…

机器学习 · 计算机科学 2025-05-07 Da Zheng , Lun Du , Junwei Su , Yuchen Tian , Yuqi Zhu , Jintian Zhang , Lanning Wei , Ningyu Zhang , Huajun Chen

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and…

人机交互 · 计算机科学 2024-04-10 M. Namvarpour , A. Razi

Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged…

计算与语言 · 计算机科学 2023-11-28 Zishan Guo , Renren Jin , Chuang Liu , Yufei Huang , Dan Shi , Supryadi , Linhao Yu , Yan Liu , Jiaxuan Li , Bojian Xiong , Deyi Xiong

Large language models (LLMs) exhibit remarkable capabilities in question answering and reasoning thanks to their extensive parametric memory. However, their knowledge is inherently limited by the scope of their pre-training data, while…

计算与语言 · 计算机科学 2025-06-10 Atahan Özer , Çağatay Yıldız

Large Language Models (LLMs) have so far impressed the world, with unprecedented capabilities that emerge in models at large scales. On the vision side, transformer models (i.e., ViT) are following the same trend, achieving the best…

计算机视觉与模式识别 · 计算机科学 2023-10-30 Mustafa Shukor , Corentin Dancette , Matthieu Cord

Large language models (LLMs) have garnered significant attention for their superior performance in many knowledge-driven applications on the world wide web.These models are designed to train hundreds of millions or more parameters on large…

计算与语言 · 计算机科学 2025-04-09 Bingchen Liu , Yuanyuan Fang , Naixing Xu , Shihao Hou , Xin Li , Qian Li

As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grounded in science communication research, to assess LLM…

Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and analysis. Recent advancements in Artificial Intelligence…

Recent advancements in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. These emergent capabilities necessitate rigorous evaluation methods to…

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities…

计算机视觉与模式识别 · 计算机科学 2023-10-18 Zejun Li , Ye Wang , Mengfei Du , Qingwen Liu , Binhao Wu , Jiwen Zhang , Chengxing Zhou , Zhihao Fan , Jie Fu , Jingjing Chen , Xuanjing Huang , Zhongyu Wei

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their…

计算与语言 · 计算机科学 2024-03-26 Jiahui Geng , Fengyu Cai , Yuxia Wang , Heinz Koeppl , Preslav Nakov , Iryna Gurevych

Large language models (LLMs) acquire knowledge across diverse domains such as science, history, and geography encountered during generative pre-training. However, due to their stochasticity, it is difficult to predict what LLMs have…

计算与语言 · 计算机科学 2026-01-27 Kartik Sharma , Yiqiao Jin , Rakshit Trivedi , Srijan Kumar

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and…

神经与进化计算 · 计算机科学 2024-05-30 Xingyu Wu , Sheng-hao Wu , Jibin Wu , Liang Feng , Kay Chen Tan

Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities from other specialized LLMs. Popular methods like ensemble…

While pre-trained language models (LMs) have brought great improvements in many NLP tasks, there is increasing attention to explore capabilities of LMs and interpret their predictions. However, existing works usually focus only on a certain…

计算与语言 · 计算机科学 2022-07-29 Yaozong Shen , Lijie Wang , Ying Chen , Xinyan Xiao , Jing Liu , Hua Wu

Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language…

计算与语言 · 计算机科学 2025-05-15 Yidan Zhang , Yu Wan , Boyi Deng , Baosong Yang , Haoran Wei , Fei Huang , Bowen Yu , Junyang Lin , Fei Huang , Jingren Zhou

Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers…

计算与语言 · 计算机科学 2024-10-24 Zhangyin Feng , Weitao Ma , Weijiang Yu , Lei Huang , Haotian Wang , Qianglong Chen , Weihua Peng , Xiaocheng Feng , Bing Qin , Ting liu

Integrating Large Language Models (LLMs) and Evolutionary Computation (EC) represents a promising avenue for advancing artificial intelligence by combining powerful natural language understanding with optimization and search capabilities.…

神经与进化计算 · 计算机科学 2025-05-22 Dikshit Chauhan , Bapi Dutta , Indu Bala , Niki van Stein , Thomas Bäck , Anupam Yadav

The ability to build and reason about models of the world is essential for situated language understanding. But evaluating world modeling capabilities in modern AI systems -- especially those based on language models -- has proven…