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相关论文: Creating Arabic LLM Prompts at Scale

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Large language models (LLMs) have demonstrated impressive performance across a wide range of Natural Language Processing (NLP) tasks. However, ensuring their effectiveness across multiple languages presents unique challenges. Multilingual…

计算与语言 · 计算机科学 2025-05-20 Shubham Vatsal , Harsh Dubey , Aditi Singh

Large Language Models (LLMs) are now integral to numerous industries, increasingly serving as the core reasoning engine for autonomous agents that perform complex tasks through tool-use. While the development of Arabic-native LLMs is…

Recent research has explored the creation of questions from code submitted by students. These Questions about Learners' Code (QLCs) are created through program analysis, exploring execution paths, and then creating code comprehension…

计算机与社会 · 计算机科学 2024-04-19 Teemu Lehtinen , Charles Koutcheme , Arto Hellas

The advanced large language model (LLM) ChatGPT has shown its potential in different domains and remains unbeaten due to its characteristics compared to other LLMs. This study aims to evaluate the potential of using a fine-tuned ChatGPT…

计算与语言 · 计算机科学 2023-12-20 Md. Rafiul Biswas , Ashhadul Islam , Zubair Shah , Wajdi Zaghouani , Samir Brahim Belhaouari

Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software development, academic research and industry based projects are…

Large Language Models (LLMs) are increasingly used by software engineers for code generation. However, limitations of LLMs such as irrelevant or incorrect code have highlighted the need for prompt programming (or prompt engineering) where…

软件工程 · 计算机科学 2025-07-09 Ranim Khojah , Francisco Gomes de Oliveira Neto , Mazen Mohamad , Philipp Leitner

With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural language and the complexity of rule sets, especially in long…

计算与语言 · 计算机科学 2025-04-21 Teng Wang , Zhenqi He , Wing-Yin Yu , Xiaojin Fu , Xiongwei Han

This paper presents a novel approach to fine-tuning the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with only 4GB VRAM. We detail the process of adapting this large language model…

计算与语言 · 计算机科学 2024-12-24 Prakash Aryan

Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language…

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as…

人机交互 · 计算机科学 2024-05-08 Jing Wei , Sungdong Kim , Hyunhoon Jung , Young-Ho Kim

This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. Significant concerns…

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that…

机器人学 · 计算机科学 2023-10-11 Juo-Tung Chen , Chien-Ming Huang

In this paper we introduce APL (Arabic Programming Language) that uses Large language models (LLM) as semi-compiler to covert Arabic text code to python code then run the code. Designing a full pipeline from the structure of the APL text…

软件工程 · 计算机科学 2024-03-26 Serry Sibaee , Omar Najar , Lahouri Ghouti , Anis Koubaa

Large Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from natural language (NL) instructions. However, studies have questioned their ability to produce…

软件工程 · 计算机科学 2025-02-27 Catherine Tony , Nicolás E. Díaz Ferreyra , Markus Mutas , Salem Dhiff , Riccardo Scandariato

Large Language Models (LLMs) have the potential to revolutionize automated traceability by overcoming the challenges faced by previous methods and introducing new possibilities. However, the optimal utilization of LLMs for automated…

软件工程 · 计算机科学 2023-08-02 Alberto D. Rodriguez , Katherine R. Dearstyne , Jane Cleland-Huang

Large Language Models (LLMs) have upended decades of pedagogy in computing education. Students previously learned to code through \textit{writing} many small problems with less emphasis on code reading and comprehension. Recent research has…

Large Language Models (LLMs) exhibit remarkable proficiency in addressing a diverse array of tasks within the Natural Language Processing (NLP) domain, with various prompt design strategies significantly augmenting their capabilities.…

计算与语言 · 计算机科学 2024-08-05 Xiangyu Zhao , Chengqian Ma

While large language models (LLMs) such as ChatGPT and PaLM have demonstrated remarkable performance in various language understanding and generation tasks, their capabilities in complex reasoning and intricate knowledge utilization still…

计算与语言 · 计算机科学 2023-10-11 Haodi Zhang , Min Cai , Xinhe Zhang , Chen Jason Zhang , Rui Mao , Kaishun Wu

The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People can now use natural language (i.e. prompts) to communicate…

软件工程 · 计算机科学 2023-12-21 Yu Cheng , Jieshan Chen , Qing Huang , Zhenchang Xing , Xiwei Xu , Qinghua Lu

Recent breakthroughs in Large Language Models (LLMs), such as GPT-3 and Codex, now enable software developers to generate code based on a natural language prompt. Within computer science education, researchers are exploring the potential…

计算机与社会 · 计算机科学 2022-12-13 Stephen MacNeil , Andrew Tran , Juho Leinonen , Paul Denny , Joanne Kim , Arto Hellas , Seth Bernstein , Sami Sarsa