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The development of large language models (LLMs) has been catalyzed by advancements in pre-training techniques. These models have demonstrated robust reasoning capabilities through manually designed prompts. In this work, we evaluate the…

计算与语言 · 计算机科学 2024-11-18 Yuxuan Huang

Healthcare systems around the world are grappling with issues like inefficient diagnostics, rising costs, and limited access to specialists. These problems often lead to delays in treatment and poor health outcomes. Most current AI and deep…

人工智能 · 计算机科学 2025-12-22 Maliha Tabassum , M Shamim Kaiser

Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the…

Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the performance of recent MLLM-based applications…

Large Language Models (LLMs) and AI assistants driven by these models are experiencing exponential growth in usage among both expert and amateur users. In this work, we focus on evaluating the reliability of current LLMs as science…

计算与语言 · 计算机科学 2024-09-24 Prasoon Bajpai , Niladri Chatterjee , Subhabrata Dutta , Tanmoy Chakraborty

Large language models (LLMs) have achieved remarkable success across a wide spectrum of tasks; however, they still face limitations in scenarios that demand long-term planning and spatial reasoning. To facilitate this line of research, in…

计算与语言 · 计算机科学 2025-02-25 Mohamed Aghzal , Erion Plaku , Ziyu Yao

Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still…

计算与语言 · 计算机科学 2024-08-12 Yilun Zhao , Yitao Long , Hongjun Liu , Ryo Kamoi , Linyong Nan , Lyuhao Chen , Yixin Liu , Xiangru Tang , Rui Zhang , Arman Cohan

Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API…

计算与语言 · 计算机科学 2023-05-25 Shishir G. Patil , Tianjun Zhang , Xin Wang , Joseph E. Gonzalez

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers,…

计算与语言 · 计算机科学 2024-02-08 Dorian Quelle , Alexandre Bovet

Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users. In this technical report, we take an initiative to investigate their capacities of playing text…

计算与语言 · 计算机科学 2025-04-01 Chen Feng Tsai , Xiaochen Zhou , Sierra S. Liu , Jing Li , Mo Yu , Hongyuan Mei

Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam…

计算与语言 · 计算机科学 2025-03-17 Dibyanayan Bandyopadhyay , Soham Bhattacharjee , Asif Ekbal

Generative artificial intelligence tools, like ChatGPT, are an increasingly utilized resource among computational social scientists. Nevertheless, there remains space for improved understanding of the performance of ChatGPT in complex tasks…

计算与语言 · 计算机科学 2025-12-02 Breanna E. Green , Ashley L. Shea , Pengfei Zhao , Drew B. Margolin

Purpose: To assess the alignment of GPT-4-based evaluation to human clinician experts, for the evaluation of responses to ophthalmology-related patient queries generated by fine-tuned LLM chatbots. Methods: 400 ophthalmology questions and…

Causal reasoning ability is crucial for numerous NLP applications. Despite the impressive emerging ability of ChatGPT in various NLP tasks, it is unclear how well ChatGPT performs in causal reasoning. In this paper, we conduct the first…

计算与语言 · 计算机科学 2023-10-13 Jinglong Gao , Xiao Ding , Bing Qin , Ting Liu

Large language models have demonstrated remarkable few-shot performance on many natural language understanding tasks. Despite several demonstrations of using large language models in complex, strategic scenarios, there lacks a comprehensive…

The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their…

机器学习 · 计算机科学 2025-02-13 Lejla Skelic , Yan Xu , Matthew Cox , Wenjie Lu , Tao Yu , Ruonan Han

Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recent works have studied these models for different scenarios…

Recent advances in reasoning with large language models (LLMs)has shown remarkable reasoning capabilities in domains such as mathematics and coding, yet their application to clinical diagnosis remains underexplored. Here, we introduce…

计算与语言 · 计算机科学 2025-04-16 Wuyang Lan , Wenzheng Wang , Changwei Ji , Guoxing Yang , Yongbo Zhang , Xiaohong Liu , Song Wu , Guangyu Wang

Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of…

计算机视觉与模式识别 · 计算机科学 2024-12-02 Shukang Yin , Chaoyou Fu , Sirui Zhao , Ke Li , Xing Sun , Tong Xu , Enhong Chen

In this paper, we test the hypothesis that although OpenAI's GPT-4 performs well generally, we can fine-tune open-source models to outperform GPT-4 in smart contract vulnerability detection. We fine-tune two models from Meta's Code Llama…

密码学与安全 · 计算机科学 2024-07-17 Peter Ince , Xiapu Luo , Jiangshan Yu , Joseph K. Liu , Xiaoning Du
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