llm4code

#大语言模型#[ICML'24] Magicoder: Empowering Code Generation with OSS-Instruct

#数据仓库#A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering

#大语言模型#[NeurIPS'24] SelfCodeAlign: Self-Alignment for Code Generation

#大语言模型#MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for LLMs.

#大语言模型#For our CCS24 paper 🏆 "ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries" by Danning Xie, Zhuo Zhang, Nan Jiang, Xiangzhe Xu, Lin Tan, and Xiangyu Zhang. �...

#大语言模型#✅SRepair: Powerful LLM-based Program Repairer with $0.029/Fixed Bug

#大语言模型#For our ICSE23 paper "Impact of Code Language Models on Automated Program Repair" by Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan

#大语言模型#Indexing three datasets for GPTScan

#大语言模型#For our ISSTA23 paper "How Effective are Neural Networks for Fixing Security Vulnerabilities?" by Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena S...

Flow Chart Image-to-Code Generation

#大语言模型#Official repo for "HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task"

Collections of research, benchmarks and tools towards more robust and reliable language models for code; LM4Code; LM4SE; reliable LLM; LLM4Code

#大语言模型#Code for ACL (main) paper "JumpCoder: Go Beyond Autoregressive Coder via Online Modification"

Dataflow-guided retrieval augmentation for repository-level code completion, ACL 2024 (main)

#大语言模型#Can Language Models Replace Programmers? RepoCod Says ‘Not Yet’ - by Shanchao Liang and Yiran Hu and Nan Jiang and Lin Tan

Simultaneous evaluation on both functionality and security of LLM-generated code.

[AAAI 2025] The official code of the paper "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct"(https://arxiv.org/abs/2407.05700).

#大语言模型#WAFFLE: Multi-Modal Model for Automated Front-End Development - by Shanchao Liang and Nan Jiang and Shangshu Qian and Lin Tan

#大语言模型#For our AAAI25 paper LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement by Nan Jiang, Shanchao Liang, Chengxiao Wang, Jiannan Wang, and Lin Tan

Replication package for the paper: "How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning"