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Large language models (LLMs) have brought significant advancements to code generation and code repair, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like…

软件工程 · 计算机科学 2024-07-08 Jiexin Wang , Xitong Luo , Liuwen Cao , Hongkui He , Hailin Huang , Jiayuan Xie , Adam Jatowt , Yi Cai

Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-of-the-art code LMs (e.g., Codex (Chen et al., 2021)) are not…

编程语言 · 计算机科学 2022-05-05 Frank F. Xu , Uri Alon , Graham Neubig , Vincent J. Hellendoorn

Recent development of large language models (LLMs) for code like CodeX and CodeT5+ demonstrates tremendous promise in achieving code intelligence. Their ability of synthesizing code that completes a program for performing a pre-defined task…

计算与语言 · 计算机科学 2023-10-10 Weimin Xiong , Yiwen Guo , Hao Chen

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding…

General large language models (LLMs), represented by ChatGPT, have demonstrated significant potential in tasks such as code generation in software engineering. This has led to the development of specialized LLMs for software engineering,…

软件工程 · 计算机科学 2024-01-09 Zibin Zheng , Kaiwen Ning , Yanlin Wang , Jingwen Zhang , Dewu Zheng , Mingxi Ye , Jiachi Chen

Translation between natural language and source code can help software development by enabling developers to comprehend, ideate, search, and write computer programs in natural language. Despite growing interest from the industry and the…

Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applications ages, it is now more critical than ever to increase…

Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning. However, their continued advancement is constrained by the scarcity of high-quality, publicly…

Evaluating the performance of Code Language Models (CLMs) for software engineering tasks, especially in multilingual and low-resource programming language settings, poses significant challenges. These challenges are primarily due to the…

软件工程 · 计算机科学 2024-11-26 Rohit Dandamudi , Gema Rodríguez-Pérez

Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate…

软件工程 · 计算机科学 2025-04-03 Nam Huynh , Beiyu Lin

In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limited scenarios, such as code generation or completion, which…

软件工程 · 计算机科学 2024-09-17 Jia Feng , Jiachen Liu , Cuiyun Gao , Chun Yong Chong , Chaozheng Wang , Shan Gao , Xin Xia

We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following…

Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language…

计算与语言 · 计算机科学 2024-02-27 Haoyang Li , Jing Zhang , Hanbing Liu , Ju Fan , Xiaokang Zhang , Jun Zhu , Renjie Wei , Hongyan Pan , Cuiping Li , Hong Chen

Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and generation capacities…

计算与语言 · 计算机科学 2024-06-10 Weixiang Yan , Haitian Liu , Yunkun Wang , Yunzhe Li , Qian Chen , Wen Wang , Tingyu Lin , Weishan Zhao , Li Zhu , Hari Sundaram , Shuiguang Deng

While Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, they often produce solutions that lack guarantees of correctness, robustness, and efficiency. This limitation is particularly acute in domains…

软件工程 · 计算机科学 2025-09-04 Yueke Zhang , Yifan Zhang , Kevin Leach , Yu Huang

Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments.…

计算与语言 · 计算机科学 2023-11-07 Mohammad Abdullah Matin Khan , M Saiful Bari , Xuan Long Do , Weishi Wang , Md Rizwan Parvez , Shafiq Joty

In this paper, we introduce CodingTeachLLM, a large language model (LLM) designed for coding teaching. Specially, we aim to enhance the coding ability of LLM and lead it to better teaching mode in education context. Thus, we propose an…

机器学习 · 计算机科学 2025-04-02 Zhangquan Chen , Chunjiang Liu , Haobin Duan

We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for training Large Language Models for coding. Existing…

机器学习 · 计算机科学 2025-07-15 Zhangchen Xu , Yang Liu , Yueqin Yin , Mingyuan Zhou , Radha Poovendran

Large language models (LLMs) play a crucial role in software engineering, excelling in tasks like code generation and maintenance. However, existing benchmarks are often narrow in scope, focusing on a specific task and lack a comprehensive…