Awesome-Code-LLM
[TMLR] A curated list of language modeling researches for code and related datasets.
https://github.com/codefuse-ai/Awesome-Code-LLM
Last synced: 5 days ago
JSON representation
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5. Methods/Models for Downstream Tasks
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Code RAG
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Code Commenting and Summarization
- 2024-10
- 2024-10
- 2020-05
- 2020-12
- 2021-04
- 2022-03
- 2023-03
- 2023-05
- 2023-08
- 2023-08
- 2024-04
- 2024-10
- 2025-01
- 2024-10
- 2025-02
- 2025-02
- 2024-06
- 2024-06
- 2024-09
- 2024-07
- 2024-08
- 2024-04
- 2024-10
- 2024-08
- 2024-10
- 2022-05
- 2024-10
- 2025-01
- 2024-09
- 2024-08
- 2024-10
- 2024-04
- 2024-10
- 2024-12
- 2024-05 - Mint/DocuMint)]
- 2024-05
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-10
- 2024-10
- 2024-06
- 2024-07
- 2024-10
- 2024-10
- 2024-10
- 2024-12
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-01
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2025-02
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Code Similarity and Embedding (Clone Detection, Code Search)
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Text-To-SQL
- 2024-02
- 2024-07
- 2024-05
- 2024-05
- 2024-05
- 2024-11
- 2024-11
- 2024-02
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2024-06
- 2024-06
- 2024-06
- 2024-08
- 2024-08
- 2024-08
- 2024-07
- 2024-07
- 2024-04
- 2024-04
- 2024-04
- 2024-05
- 2024-08
- 2024-08
- 2024-02
- 2024-08
- 2024-08
- 2025-03
- 2025-03
- 2025-03
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-08
- 2024-02
- 2024-02
- 2024-02
- 2024-08
- 2024-08
- 2025-03
- 2024-08
- 2024-11
- 2024-11
- 2024-04
- 2021-09
- 2022-04
- 2022-09
- 2022-10
- 2022-10
- 2023-03
- 2023-04
- 2023-05
- 2023-05
- 2023-05
- 2023-07
- 2023-08
- 2024-03
- 2024-05
- 2024-10
- 2024-09
- 2024-09
- 2024-12
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-05
- 2024-10
- 2024-11
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-10
- 2024-12
- 2024-12
- 2023-12
- 2024-10
- 2024-10
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-11
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-02
- 2025-03
- 2025-03
- 2025-03
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Vulnerability Detection
- 2024-05
- 2024-11
- 2024-11
- 2024-11
- 2024-12
- 2025-02
- 2024-06
- 2024-09
- 2024-07
- 2024-07
- 2024-07
- 2021-05
- 2021-06
- 2021-10
- 2022-01
- 222-04
- 2022-05
- 2022-05
- 2022-09
- 2022-12
- 2023-05
- 2023-06
- 2023-08
- 2023-08
- 2023-10
- 2023-11
- 2023-12
- 2024-01
- 2024-01
- 2024-02
- 2024-03
- 2024-04
- 2024-05
- 2024-05
- 2024-08
- 2024-08
- 2025-03
- 2024-08
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-10
- 2024-02
- 2025-01
- 2025-01
- 2024-08
- 2024-08
- 2024-11
- 2024-11
- 2024-09
- 2024-04
- 2024-04
- 2024-04
- 2024-03
- 2018-04
- 2020-01
- [paper
- 2024-05
- 2024-10
- 2019-10
- 2024-09
- 2024-09
- 2024-09
- 2024-12
- 2024-12
- 2023-01
- 2024-10
- 2024-10
- 2024-06
- 2024-06
- 2024-06
- 2024-05
- 2024-11
- 2024-06
- 2024-10
- 2024-10
- 2024-12
- 2024-06
- 2024-07
- 2024-11
- 2024-11
- 2025-02
- 2025-02
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-03
- 2025-03
- 2025-02
- 2025-02
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Program Repair
- 2024-05
- 2024-05
- 2024-05
- 2024-04
- 2021-05
- 2021-06
- 2022-05
- 2022-07
- 2022-08
- 2022-10
- 2023-01
- 2023-02
- 2023-03
- 2023-04
- 2023-04
- 2023-06
- 2025-01
- 2025-01
- 2024-04
- 2024-04
- 2024-07
- 2024-09
- 2024-09
- 2024-08
- 2024-09
- 2024-05
- 2024-04
- 2024-04
- 2024-05
- 2025-03
- 2024-07
- 2025-01
- 2024-09
- 2024-08
- 2024-08
- 2024-08
- 2025-03
- 2024-08
- 2022-11
- 2023-12
- 2024-04
- 2024-04
- 2024-10
- 2024-09
- 2024-09
- 2024-09
- 2025-02
- 2025-02
- 2024-11
- 2024-06
- 2024-10
- 2024-10
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2021-02
- 2025-03
- 2025-02
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Code Review
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Code Translation
- 2024-05
- 2025-01
- 2025-01
- 2024-08
- 2024-08
- 2024-06
- 2024-04
- 2024-04
- 2024-07
- 2023-10
- 2024-03
- 2018-02
- 2018-07
- 2021-10
- 2022-06
- 2022-07
- 2023-02
- 2023-06
- 2023-08
- 2023-11
- 2024-05
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-12
- 2024-12
- 2024-12
- 2024-11
- 2024-07
- 2024-11
- 2024-12
- 2025-01
- 2025-01
- 2025-03
- 2025-03
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Repository-Level Coding
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Compiler Optimization
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Oracle Generation
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Frontend Development
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Program Proof
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Code Generation
- 2024-11
- 2024-11
- 2024-06
- 2024-12
- 2024-04
- 2024-03
- 2025-01
- 2024-06
- 2024-07
- 2024-09
- 2024-09
- 2024-08
- 2023-11
- 2024-04
- 2023-09
- 2024-01
- 2024-08
- 2024-07
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-10
- 2024-08
- 2024-08
- 2024-03
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-11
- 2024-11
- 2024-11
- 2024-04
- 2024-04
- 2024-11
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-10
- 2024-10
- 2024-10
- 2024-11
- 2024-06
- 2024-12
- 2024-06
- 2024-11
- 2024-10
- 2025-02
- 2025-02
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-02
- 2025-03
- 2025-03
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Fuzz Testing
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Malicious Code Detection
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-08
- 2024-04
- [paper
- [paper
- 2024-09
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2023-08
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2023-03
- 2023-05
- 2023-08
- 2023-12
- 2023-12
- 2024-03
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-07
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2025-03
- [paper
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Requirement Engineering
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Type Prediction
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Test Generation
- 2024-04
- 2025-01
- 2023-05
- 2025-02
- 2024-06
- 2024-06
- 2024-09
- 2024-04
- 2024-08
- 2024-07
- 2024-09
- 2024-09
- 2024-10
- 2024-08
- 2024-11
- 2024-09
- 2024-04
- 2023-02
- 2023-02
- 2023-04
- 2023-10
- 2024-04
- 2023-08
- 2023-08
- 2020-09
- 2023-02
- 2023-05
- 2023-05
- 2023-07
- 2023-07
- 2023-10
- 2024-03
- 2024-12
- 2024-05
- 2024-04
- 2024-04
- 2024-09
- 2024-09
- 2024-09
- 2024-09
- 2024-12
- 2025-02
- 2025-01
- 2024-11
- 2024-11
- 2024-06
- 2024-06
- 2024-10
- 2024-10
- 2024-12
- 2024-12
- 2024-12
- 2024-06
- 2024-06
- 2024-06
- 2024-11
- 2024-07
- 2024-07
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2023-10
- 2025-03
- 2025-03
- 2025-03
- 2025-03
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Code Refactoring and Migration
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Mutation Testing
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Binary Analysis and Decompilation
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Automated Machine Learning
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Software Configuration
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Log Analysis
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Code Ranking
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Commit Message Generation
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Software Modeling
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Code QA & Reasoning
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3. When Coding Meets Reasoning
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3.1 Coding for Reasoning
- 2024-02
- 2024-02
- 2024-11
- 2024-01
- 2024-01
- 2023-08
- 2023-10
- 2024-05
- 2024-05
- 2024-12
- 2024-08
- 2024-08
- 2024-07
- 2024-01
- 2024-02
- 2024-07
- 2024-03
- 2024-02
- 2024-03
- 2024-07
- 2024-07
- 2022-11 - machines/pal)]
- 2022-11 - of-Thoughts)]
- 2023-12
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-01
- 2023-05
- 2024-11
- 2024-11
- 2025-02
- 2025-02
- 2025-02
- 2024-04
- 2024-09
- 2025-02
- 2025-02
- 2024-11
- 2024-10
- 2024-09
- 2024-10
- 2024-05
- 2024-05
- 2024-10
- 2024-06
- 2024-10
- 2024-12
- 2024-07
- 2024-11
- 2025-02
- 2024-12
- 2025-01
- 2025-01
- 2025-02
- 2025-03
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3.3 Code Agents
- 2024-11
- 2023-10
- 2024-04
- 2024-04
- 2025-01
- 2025-02
- 2025-02
- 2024-06
- 2024-03
- 2024-05
- 2024-09
- 2024-08
- 2024-06
- 2024-03
- 2024-01
- 2024-07
- 2023-04
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-09 - websoft/PairCoder)]
- 2024-11
- 2024-11
- 2025-02
- 2023-07
- 2023-08
- 2024-03
- 2024-03
- 2024-05
- 2024-10
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-05
- 2024-05
- 2024-10
- 2024-10
- 2024-06
- 2024-06
- 2024-06
- 2024-10
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-03
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3.5 Frontend Navigation
- 2024-11
- 2024-11
- 2024-11
- 2024-11
- 2025-01
- 2024-04
- 2021-10
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-04
- 2025-03
- 2024-04
- 2023-07
- 2021-10
- 2021-12
- 2022-01
- 2022-01
- 2022-02
- 2022-02
- 2022-07
- 2022-10
- 2022-10
- 2023-01
- 2023-06
- 2023-07
- 2023-12
- 2024-01
- 2024-01
- 2024-02
- 2024-02
- 2024-10
- 2024-10
- 2024-10
- 2024-09
- 2024-12
- 2024-11
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-06
- 2024-12
- 2024-12
- 2024-10
- 2024-11
- 2024-12
- 2025-01
- 2025-01
- 2025-01
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3.4 Interactive Coding
- 2024-11
- 2024-11
- 2024-11
- 2024-05
- 2023-06
- 2020-06
- 2022-08
- 2023-03
- 2023-03
- 2023-04
- 2023-05
- 2024-03
- 2025-02
- 2024-06
- 2024-08
- 2024-07
- 2023-11
- 2017-03
- 2023-06
- 2024-02
- 2024-11
- 2024-04
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2023-05
- 2024-03
- 2024-09
- 2024-12
- 2025-02
- 2025-02
- 2025-02
- 2024-10
- 2024-05
- 2024-05
- 2024-05
- 2024-10
- 2024-11
- 2025-02
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-03
- 2025-03
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3.2 Code Simulation
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4. Datasets
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4.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - dot-jar/bugs-dot-jar)] |
- [paper
- [paper
- [paper - tan/CoCoNut-Artifact)] |
- [paper
- [paper - USZ/FixJS)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - code-search)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
-
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8. Datasets
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8.2 Benchmarks
- [paper
- [paper
- [paper
- [paper
- [paper - benchmarks/tree/main/MBUPP)] |
- [paper - Eval)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - li/CleanVul)] |
- [paper - swe-bench/multi-swe-bench.github.io)] |
- [paper - code)] |
- [paper
- [paper
- [paper - data/)] |
- [paper - lily.github.io/spider)] |
- [paper
- [paper - codechanges)] |
- [paper
- [paper - group/mineSStuBs)] |
- [paper
- [paper - KTH/megadiff)] |
- [paper
- [paper
- [paper
- [paper
- [paper - docstring-corpus)] |
- [paper
- [paper
- [paper
- [paper - group/diversevul)] |
- [paper
- [paper - Targaryen/MC-Evaluation)] |
- [paper - 810A)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Coder/tree/main/qwencoder-eval/instruct/CodeArena)] |
- [paper - Lab/BookSQL)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02
- [paper - dougherty/fvapps)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - NLP/novicode)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - sri/TFix)] |
- [paper
- 2024-02
- [paper
- [paper - bench/SciCode)] |
- [paper
- [paper
- [paper - team/coir)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2023-11
- 2024-08
- [paper
- [paper
- [paper - ai/WebApp1K-React)] |
- [paper
- [paper
- [paper - research/google-research/tree/master/mbpp)] [[MathQA-Python](https://github.com/google/trax/blob/master/trax/examples/MathQA_Python_generation_notebook.ipynb)] |
- [paper
- [paper
- [paper
- [paper - fixes)] |
- [paper - bugs/bears-benchmark)] |
- [paper
- [paper - hu/TL-CodeSum)] |
- [paper
- [paper - kb/tree/main/MSR2019)] |
- [paper
- [paper
- [paper - lab.org/projects/TypeWriter/data.tar.gz)] |
- [paper
- [paper
- [paper - types-4-py-dataset)] |
- [paper - group/CoDiSum)] |
- [paper
- [paper - autosuggestions)] |
- [paper
- [paper - Research/commit_message_generation)] |
- [paper
- [paper
- [paper
- [paper - jie-Huang/CoCoNote)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - V/HumanEval-V-Benchmark)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - us/download/103554)] |
- [paper - EA6F/)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - group/TypeT5)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - eval)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Research/CodeJudge-Eval)] |
- [paper
- [paper
- [paper - dot-jar/bugs-dot-jar)] |
- [paper
- [paper - USZ/FixJS)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - X/cruxeval-x)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2023-10 - ai/codefuse-evaluation)]
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - AES-AI4Code/CodeQuestionAnswering)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-04
- [paper
- [paper
- [paper - nlp/USACO)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - lily.github.io/sparc)] |
- [paper - lily.github.io/cosql)] |
- [paper
- [paper
- [paper
- [paper - lab-code-research/XLCoST)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Question-Code-Dataset)] |
- [paper - corpus.github.io/)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - conala)] |
- [paper - plugin/nl2code-dataset)] |
- [paper - E)] |
- [paper - science/mxeval)] |
- [paper - jie-Huang/ExeDS)] |
- [paper - ai/DS-1000)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - DK)] |
- [paper - SpiderCG)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper - Code-Search-Evaluation-Dataset)] |
- [paper - LAB-SJTU/CosBench/wiki)] |
- [paper
- [paper
- [paper - Code/NL-code-search-WebQuery)] |
- [paper
- [paper
- [paper
- [paper - easel/StudentEval)] |
- [paper
- [paper - bench.github.io/)] |
- [paper
- [paper - 0/commit0)] |
- 2024-11
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - gmu/mHumanEval)] |
- [paper
- [paper
- [paper - nl2sql)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02
- [paper - eval)] |
- [paper - deepmind/code_contests)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - xl)] |
- [paper
- [paper - ai/Spider2)] |
- [paper - level-Vulnerability-Detection)] |
- 2025-02 - deepmind/bbeh)]
- 2025-02
- 2025-02
- 2025-02
- [paper - Bench-D65E/README.md)] |
- [paper - XL)] |
- [paper - ai/geospatial-code-llms-dataset)] |
- [paper - AI4Code/CodeMMLU)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Bench)] |
- [paper
- [paper
- 2024-10
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Eval-Team/M2RC-Eval)] |
- [paper
- 2024-06 - rag-bench/code-rag-bench)]
- [paper - bench/JavaBench)] |
- [paper
- 2024-06 - Research/lca-baselines)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-12 - eval)]
- [paper
- [paper - codes/VulDeePecker)] |
- [paper
- [paper - Research/plot_bench)] |
- [paper - Coder/tree/main/qwencoder-eval/instruct/CodeArena)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-06
- [paper - project/bigcodebench)] |
- [paper
- [paper - AI/RES-Q)] |
- [paper
- [paper
- [paper
- [paper - eval)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - liuzy/CodeUpdateArena)] |
- [paper
- [paper
- [paper
- [paper - tan/CoCoNut-Artifact)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2025-02
- [paper - Benchmark)] |
- 2020-09
- 2023-02
- [paper - sudo/DependEval)] |
- [paper - lab-code-research/MuST-CoST)] |
- [paper - TransEval)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02
- 2025-02
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Pro/CodeEval-Pro/tree/main)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Bench)] |
- 2025-01
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - code-search)] |
- 2025-01
- [paper
- [paper
- [paper
- [paper - hu/DeepCom)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - benchmark)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Benchmark/Tests-C250)] |
- [paper - 7B74/README.md)] |
- [paper - 9/probench)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
-
8.1 Pretraining
-
-
2. Models
-
2.5 Reinforcement Learning on Code
-
2.2 Existing LLM Adapted to Code
-
2.1 Base LLMs and Pretraining Strategies
- 2024-05 - 7B)]
- 2025-01
- 2024-06
- 2024-04 - ai/JetMoE)]
- 2022-04 - neox)]
- 2023-03
- 2023-09 - 1_5)]
- 2023-09 - inc/Baichuan2)]
- 2023-09
- 2024-01 - of-experts/)]
- 2024-01
- 2024-06
- 2024-11
- 2025-01
- 2024-06
- 2024-06
- 2024-06
- 2024-07
- 2024-07
- 2024-09
- 2024-07
- 2024-05 - ai/DeepSeek-V2)]
- 2024-04
- 2024-04
- 2024-04 - FLM)]
- 2024-07
- 2024-04 - llama/llama3)] [[paper](https://arxiv.org/abs/2407.21783)]
- 2023-10 - src)]
- 2023-12
- 2023-12
- 2023-12 - research/YAYI2)]
- 2024-01 - ai/DeepSeek-LLM)]
- 2024-07
- 2024-02
- 2024-01 - ai/DeepSeek-MoE)]
- 2022-01
- 2023-07
- 2024-02 - open-models/)]
- 2024-04
- 2024-06
- 2024-09
- 2024-08
- 2024-09
- 2024-11
- 2024-04 - 34B)]
- 2024-12
- 2024-03 - ai/Yi)]
- 2024-03 - 3-family)]
- 2025-02
- 2025-03
- 2024-12
- 2024-12
- 2025-02
- 2024-11
- 2024-10
- 2024-05 - art-projection/MAP-NEO)]
- 2024-10
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-12
- 2024-12
- 2024-12
- 2024-12
- 2024-06
- 2024-11
- 2025-03
- 2024-12
- 2024-12
- 2024-12
- 2025-02
- 2025-04
- 2025-03
-
2.4 (Instruction) Fine-Tuning on Code
- 2023-09
- 2023-11
- 2024-05
- 2024-05
- 2023-06
- 2023-07
- 2023-11 - ai/MFTCoder)]
- 2024-04
- 2024-02
- 2024-06
- 2024-06
- [paper
- 2024-07
- 2023-12
- 2024-02
- 2024-04
- 2024-04 - uiuc/xft)]
- 2024-07
- 2024-08
- ACL 2024 Findings
- 2024-09
- 2024-09
- 2024-09
- 2024-11
- 2025-03
- 2023-12
- 2024-01
- 2024-03
- 2025-03
- 2024-12
- 2025-02
- 2025-02
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-05
- 2024-10
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-10
- 2024-07
- 2024-07
- 2024-11
- 2025-01
- 2024-06
- 2025-03
- 2025-04
- 2025-04
-
2.3 General Pretraining on Code
- 2019-12 - research/google-research/tree/master/cubert)]
- 2020-02
- 2020-09
- 2021-08
- 2021-10
- 2022-05
- 2020-05
- 2021-12
- 2022-02 - LMs)]
- 2022-03
- 2022-04
- 2022-06
- 2022-07
- 2023-01
- 2020-10
- 2021-02 - mastropaolo/TransferLearning4Code)]
- 2024-02
- 2024-09
- 2021-02
- 2021-03
- 2021-09
- 2022-01
- 2022-06
- 2023-05
- 2020-12
- 2022-03
- 2024-05 - 07] [[paper](https://arxiv.org/abs/2407.13739)]
- 2024-01 - ai/DeepSeek-Coder)]
- 2024-02
- 2024-01
- 2024-10
- 2024-11
- 2023-05
- 2023-06 - 1)]
- 2024-04
- 2024-03
- 2022-12 - code)]
- 2024-07
- 2025-01
- 2025-03
-
-
7. Human-LLM Interaction
-
Others
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2024-04
- 2024-04
- 2025-01
- 2024-04
- 2024-05
- 2024-04
- 2024-07
- 2024-08
- 2024-07
- 2024-09
- 2024-09
- 2024-04
- 2024-09
- 2024-10
- 2024-07
- 2025-01
- 2025-01
- 2024-09
- 2022-06
- 2022-10
- 2023-02
- 2023-02
- 2023-04
- 2023-08
- 2023-09
- 2023-09
- 2023-10
- 2024-04
- 2024-11
- 2024-05
- 2024-10
- 2024-10
- 2022-04
- 2024-09
- 2024-11
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-06
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-10
- 2024-10
- 2024-12
- 2024-12
- 2024-06
- 2024-07
- 2024-11
- 2025-01
- 2025-02
- 2025-02
- 2025-02
- 2025-01
- 2025-02
- 2025-02
- 2025-03
-
-
6. Analysis of AI-Generated Code
-
AI-Generated Code Detection
-
Robustness
-
Others
-
Correctness
-
Security and Vulnerabilities
- 2024-11
- 2024-10
- 2024-06
- 2025-01
- 2025-02
- 2024-07
- 2024-07
- 2024-07
- 2024-05
- 2023-02
- 2023-12
- 2024-04
- 2024-04
- 2024-04
- 2024-08
- 2024-08
- 2024-09
- 2024-10
- 2024-08
- 2024-03
- 2024-08
- 2024-03
- 2024-04
- 2021-08
- 2022-04
- 2022-08
- 2022-1
- 2024-05
- 2024-10
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-12
- 2024-07
- 2024-11
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
-
Efficiency
-
Bias
-
Interpretability
-
Privacy
-
API Usage
-
Hallucination
-
-
4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
-
3.5 Frontend Navigation
- **MUMPS, ALC**
- **Power Query M, OfficeScript, Excel formulas**
- **Verilog**
- **Verilog**
- **Verilog**
- **Kotlin, Swift, and Rust**
- **Verilog**
- **Verilog**
- **Verilog**
- **R**
- **Fortran, Julia, Matlab, R, Rust**
- **OpenAPI**
- 2024-06
- 2024-07
- **Verilog**
- **Verilog**
- **MaxMSP, Web Audio**
- **Verilog**
- **Bash**
- **RPA**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **Alloy**
- **Haskell**
- **Verilog**
- **Verilog**
- **Racket, OCaml, Lua, R, Julia**
- **Ruby**
- **Verilog**
- **Hansl**
- **PLC**
- **Lua**
- 2024-10
- 2024-10
- **R, D, Racket, Bash**
- **Verilog**
- **Verilog**
- **F***
- **Survey**
- **OCL**
- **Ansible-YAML**
- **Verilog**
- **Kotlin**
- **Verilog**
- **Bash**
- **Verilog**
- **SPICE**
- **IEC 61131-3 ST**
- **Verilog**
- **Logo**
- **R**
- 2024-10
- **ST**
- **Ansible YAML, Bash**
- **Qiskit**
- **Perl, Golang, Swift**
- **Verilog**
- **Json, XLM, YAML**
- **Verilog**
- 2025-02
- **Alloy***
- **Verilog**
- **HPC**
- **UCLID5**
- **G**
- **Julia, Lua, R, Racket**
- **Verilog**
- **Modelica**
- **Excel**
- **Solidity**
- **PennyLane**
-
-
5. Datasets
-
5.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
-
-
1. Surveys
-
9. Recommended Readings
-
8.2 Benchmarks
- Mixed Precision Training
- Neural Machine Translation by Jointly Learning to Align and Translate - decoder RNN |
- Neural Machine Translation of Rare Words with Subword Units - pair encoding: split rare words into subword units |
- Attention Is All You Need - attention for long-range dependency and parallel training |
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models - knowledge and complex reasoning benchmark |
- Emergent Abilities of Large Language Models
- Scaling Instruction-Finetuned Language Models
- Self-Instruct: Aligning Language Models with Self-Generated Instructions - generated data |
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- Improving Language Understanding by Generative Pre-Training - finetuning paradigm applied to Transformer decoder |
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Language Models are Unsupervised Multitask Learners
- SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models - efficient distributed optimization |
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer - decoder pretrained with an MLM-like denoising objective |
- Language Models are Few-Shot Learners - 2 (175B), they discovered a new learning paradigm: In-Context Learning (ICL) |
- Measuring Massive Multitask Language Understanding - knowledge and complex reasoning benchmark |
- LoRA: Low-Rank Adaptation of Large Language Models - efficient finetuning |
- Finetuned Language Models Are Zero-Shot Learners - finetuning |
- Multitask Prompted Training Enables Zero-Shot Task Generalization
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - of-Though reasoning |
- Training language models to follow instructions with human feedback - 3 instruction finetuned with RLHF (reinforcement learning from human feedback) |
- Training Compute-Optimal Large Language Models
- Large Language Models are Zero-Shot Reasoners
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- PaLM: Scaling Language Modeling with Pathways
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model - source dense LLM, trained on 46 languages, with detailed discussion about training and evaluation |
- LLaMA - 4](https://arxiv.org/abs/2303.08774) or [PaLM 2](https://arxiv.org/abs/2305.10403). For comprehensive reviews on these more general topics, we refer to other sources such as [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM), [Awesome AIGC Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials), or for LLM applications in other specific domains: [Awesome Domain LLM](https://github.com/luban-agi/Awesome-Domain-LLM), [Awesome Tool Learning](https://github.com/luban-agi/Awesome-Tool-Learning#awesome-tool-learning), [Awesome-LLM-MT](https://github.com/hsing-wang/Awesome-LLM-MT), [Awesome Education LLM](https://github.com/Geralt-Targaryen/Awesome-Education-LLM).
-
-
News
- Best Practices and Lessons Learned on Synthetic Data for Language Models
- MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
- 2024/06
- 2024/09/06
- Evaluating Frontier Models for Dangerous Capabilities
- LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
- Exploring Language Model's Code Generation Ability with Auxiliary Functions
- CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
- 2024/08
- The Llama 3 Herd of Models
- 2024/02
- IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
- 2024/03 - of-Experts (MoE).
- MarkLLM: An Open-Source Toolkit for LLM Watermarking
- LoRA Learns Less and Forgets Less
- 2024/10/22
- Compression Represents Intelligence Linearly
- 2024/09/14
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- 2024/07
- codefuse-ai/GALLa
- codefuse-ai/CodeFuse-CGM
- codefuse-ai/RepoFuse
- codefuse-ai/EasyDeploy
- codefuse-ai/rodimus
- codefuse-ai/CodeFuse-muAgent
- codefuse-ai/CodeFuse-CGE
- codefuse-ai/D2LLM
- codefuse-ai/CodeFuse-MFT-VLM
- codefuse-ai/MFTCoder
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Qwen2.5-Omni Technical Report
-
7. User-LLM Interaction
-
Others
-
-
6. Datasets
-
Star History
-
5.2 Benchmarks
- ![Star History Chart - history.com/#codefuse-ai/Awesome-Code-LLM&Date)
-
8.2 Benchmarks
- ![Star History Chart - history.com/#codefuse-ai/Awesome-Code-LLM&Date)
-
Programming Languages
Categories
5. Methods/Models for Downstream Tasks
958
8. Datasets
397
3. When Coding Meets Reasoning
230
2. Models
204
6. Analysis of AI-Generated Code
188
4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
75
7. Human-LLM Interaction
65
4. Datasets
37
News
35
9. Recommended Readings
32
5. Datasets
29
1. Surveys
15
6. Datasets
4
Star History
2
7. User-LLM Interaction
1
Sub Categories
8.2 Benchmarks
425
3.5 Frontend Navigation
127
Text-To-SQL
119
Vulnerability Detection
101
Others
98
Malicious Code Detection
75
2.1 Base LLMs and Pretraining Strategies
74
Test Generation
70
Code Generation
69
Program Repair
59
Code Commenting and Summarization
59
3.1 Coding for Reasoning
58
3.3 Code Agents
57
2.4 (Instruction) Fine-Tuning on Code
53
3.4 Interactive Coding
49
Security and Vulnerabilities
43
2.3 General Pretraining on Code
40
Code Translation
39
4.2 Benchmarks
37
Code Review
35
Frontend Development
34
Repository-Level Coding
33
Correctness
32
5.2 Benchmarks
30
Code Similarity and Embedding (Clone Detection, Code Search)
29
Requirement Engineering
28
2.5 Reinforcement Learning on Code
24
Log Analysis
24
Program Proof
22
Binary Analysis and Decompilation
22
AI-Generated Code Detection
17
Compiler Optimization
17
Efficiency
16
Oracle Generation
15
Code RAG
15
3.2 Code Simulation
14
Code Refactoring and Migration
14
2.2 Existing LLM Adapted to Code
13
Software Configuration
13
Automated Machine Learning
13
Code Ranking
12
Fuzz Testing
12
Software Modeling
10
Robustness
9
Interpretability
9
Hallucination
9
API Usage
8
Mutation Testing
7
Bias
7
Privacy
6
Commit Message Generation
5
8.1 Pretraining
5
6.2 Benchmarks
4
Type Prediction
4
Code QA & Reasoning
3