Awesome-Code-LLM
[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets.
https://github.com/codefuse-ai/Awesome-Code-LLM
Last synced: 4 days ago
JSON representation
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News
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models - AI.
- 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
- 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
- Instella: Fully Open Language Models with Stellar Performance
- 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
- SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
- CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
- CodeClash: Benchmarking Goal-Oriented Software Engineering
- F2LLM - ai/CodeFuse-Embeddings)] [[model & data](https://huggingface.co/collections/codefuse-ai/codefuse-embeddings-68d4b32da791bbba993f8d14)]
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1. Surveys
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3. When Coding Meets Reasoning
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3.2 Code Simulation
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3.3 Code Agents
- 2025-11
- 2025-12
- 2024-11
- 2023-10
- 2025-04
- 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
- 2025-09
- 2025-08
- 2024-05
- 2024-10
- 2024-09
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-05
- 2024-05
- 2025-05
- 2024-10
- 2024-10
- 2024-06
- 2024-06
- 2024-06
- 2024-10
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2024-11
- 2024-12
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2024-08
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-02
- 2025-07
- 2025-07
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-04
- 2025-04
- 2025-06
- 2025-05
- 2025-06
- 2025-07
- 2025-06
- 2025-06
- 2025-10
- 2025-10
- 2025-09
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-10
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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
- 2024-09
- 2024-10
- 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
- 2025-05
- 2025-05
- 2025-05
- 2025-06
- 2025-06
- 2025-02
- 2025-03
- 2025-06
- 2025-10
- 2025-09
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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
- 2025-09
- 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
- 2025-06
- 2025-08
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3.4 Interactive Coding
- 2024-11
- 2024-11
- 2024-11
- 2024-05
- 2023-06
- 2025-04
- 2020-06
- 2022-08
- 2023-03
- 2023-03
- 2023-04
- 2023-05
- 2024-03
- 2025-02
- 2024-06
- 2024-08
- 2024-07
- 2025-07
- 2023-11
- 2017-03
- 2023-06
- 2024-02
- 2024-11
- 2024-04
- 2025-02
- 2025-02
- 2025-02
- 2025-03
- 2023-05
- 2024-03
- 2025-09
- 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
- 2025-02
- 2025-03
- 2025-03
- 2025-06
- 2025-10
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4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
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3.5 Frontend Navigation
- **Triton**
- **Verilog**
- **MUMPS, ALC**
- **Power Query M, OfficeScript, Excel formulas**
- **Verilog**
- **Verilog**
- **Verilog**
- **CUDA**
- **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**
- **Hansl**
- **Ruby**
- **Verilog**
- **Verilog**
- **Verilog**
- **Racket, OCaml, Lua, R, Julia**
- 2025-08
- **Verilog**
- **CUDA**
- **LaTeX**
- **PLC**
- **Lua**
- 2024-10
- 2024-10
- **R, D, Racket, Bash**
- **Chisel**
- **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**
- **CUDA**
- 2025-02
- **Alloy***
- **Verilog**
- **HPC**
- **UCLID5**
- **Lean**
- **Verilog**
- **Verilog**
- **Verilog**
- **Triton**
- **G**
- **Julia, Lua, R, Racket**
- **SIMD intrinsics**
- **Triton**
- **Verilog**
- **Modelica**
- **Excel**
- **Solidity**
- **PennyLane**
- 2025-04
- **Verilog**
- **Verilog**
- **Verilog**
- **Verilog**
- **LaTeX**
- **CUDA**
- 2025-10
- **CUDA**
- **Verilog**
- **CUDA**
- **Triton**
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5. Methods/Models for Downstream Tasks
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Code Generation
- 2025-05
- 2025-11
- 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
- 2025-07
- 2024-04
- 2023-09
- 2024-01
- 2024-08
- 2024-07
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-10
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-08
- 2024-09
- 2024-09
- 2024-11
- 2024-11
- 2024-11
- 2024-04
- 2024-04
- 2024-11
- 2025-08
- 2025-09
- 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
- 2025-07
- 2025-07
- 2025-08
- 2025-08
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-02
- 2025-03
- 2025-03
- 2025-04
- 2025-04
- 2025-04
- 2025-07
- 2025-09
- 2025-10
- 2025-10
- 2025-10
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Code RAG
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Code Similarity and Embedding (Clone Detection, Code Search)
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Frontend Development
- 2025-08
- 2025-11
- 2024-11
- 2024-04
- 2025-02
- 2024-07
- 2024-07
- 2024-09
- 2024-09
- 2024-06
- 2024-10
- 2024-03
- 2024-09
- 2024-09
- 2024-11
- 2024-11
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-03
- [paper
- 2024-10
- 2024-10
- 2025-05
- 2024-12
- 2024-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-11
- 2025-11
- 2024-12
- 2025-05
- 2025-06
- 2025-03
- 2025-10
- 2025-10
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Text-To-SQL
- 2025-05
- 2025-05
- 2025-05
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 2025-11
- 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
- 2024-08
- 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
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2024-10
- 2025-05
- 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
- 2025-11
- 2024-11
- 2025-02
- 2025-02
- 2025-02
- 2025-02
- 2024-12
- 2024-12
- 2025-01
- 2025-01
- 2025-01
- 2025-05
- 2025-05
- 2025-05
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- 2025-05
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- 2025-06
- 2025-01
- 2025-01
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- 2025-03
- 2025-03
- 2025-03
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- 2025-03
- 2025-03
- 2024-02
- 2025-02
- 2025-06
- 2025-10
- 2025-10
- 2025-10
- 2025-09
- 2025-09
- 2025-10
- 2025-10
- 2025-11
- 2025-10
- 2025-10
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Vulnerability Detection
- 2023-01
- 2025-11
- 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
- 2024-07
- 2024-07
- 2024-07
- 2024-08
- 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
- [paper
- 2020-01
- 2025-09
- 2024-05
- 2024-10
- 2019-10
- 2025-05
- 2024-09
- 2024-09
- 2024-09
- 2024-12
- 2024-12
- 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
- 2025-05
- 2025-05
- 2025-06
- 2025-01
- 2025-01
- 2025-01
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-06
- 2025-07
- 2025-07
- 2025-02
- 2025-02
- 2025-04
- 2025-06
- 2025-06
- 2025-10
- 2025-10
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Malicious Code Detection
- [paper
- [paper
- [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
- 2024-07
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2025-03
- [paper
- 2025-04
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Commit Message Generation
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Code QA & Reasoning
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Code Commenting and Summarization
- 2024-10
- 2024-10
- 2024-10
- 2020-12
- 2021-04
- 2022-03
- 2023-03
- 2020-05
- 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
- 2024-10
- 2024-04
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- 2024-10
- 2024-08
- 2024-10
- 2022-05
- 2024-10
- 2025-01
- 2024-09
- 2024-08
- 2024-10
- 2024-04
- 2024-10
- 2024-10
- 2024-12
- 2024-05 - Mint/DocuMint)]
- 2024-05
- 2024-10
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- 2024-12
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Program Repair
- 2024-05
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- 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
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- 2024-09
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- 2025-03
- 2024-07
- 2025-01
- 2024-09
- 2024-08
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- 2024-08
- 2025-03
- 2024-08
- 2022-11
- 2023-12
- 2024-04
- 2024-04
- 2025-05
- 2025-05
- 2025-07
- 2025-07
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- 2024-11
- 2024-06
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- 2024-10
- 2024-12
- 2024-12
- 2025-03
- 2025-06
- 2025-06
- 2025-05
- 2025-06
- 2025-01
- 2025-01
- 2021-02
- 2025-03
- 2025-07
- 2025-02
- 2025-03
- 2025-11
- 2025-08
- 2025-10
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Code Review
- 2024-05
- 2024-02
- 2024-11
- 2025-01
- 2025-01
- 2025-02
- 2024-07
- 2022-01
- 2022-08
- 2023-02
- 2023-08
- 2024-04
- 2024-08
- 2025-01
- 2024-11
- 2025-09
- 2025-09
- 2024-09
- 2024-12
- 2024-12
- 2024-12
- 2024-11
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-12
- 2024-06
- 2024-07
- 2024-07
- 2025-11
- 2024-12
- 2025-01
- 2025-05
- 2025-05
- 2025-01
- 2025-01
- 2025-02
- 2025-02
- 2025-03
- 2025-07
- 2025-10
- 2025-11
-
Code Translation
- 2024-05
- 2025-01
- 2025-01
- 2024-08
- 2024-08
- 2024-06
- 2025-07
- 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
- 2025-05
- 2025-05
- 2025-05
- 2025-01
- 2025-01
- 2025-03
- 2025-03
- 2025-04
- 2025-10
-
Repository-Level Coding
- 2023-05
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-06
- 2024-03
- 2024-04
- 2024-05
- 2025-03
- 2025-04
- 2024-09
- 2024-03
- 2022-06
- 2022-12
- 2024-03
- 2023-12
- 2025-09
- 2025-09
- 2025-09
- 2024-10
- 2024-08 - philia/CoEdPilot)]
- 2024-06
- 2024-06
- 2024-05
- 2024-11
- 2024-01
- 2024-12
- 2024-12
- 2025-11
- 2025-05
- 2025-05
- 2025-03
- 2025-08
- 2025-02
- 2025-05
- 2024-07
- 2025-10
-
Compiler Optimization
-
Oracle Generation
-
Program Proof
-
Fuzz Testing
-
Requirement Engineering
-
Issue Resolution
-
Automated Machine Learning
-
Type Prediction
-
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
- 2024-03
- 2023-05
- 2023-05
- 2023-07
- 2023-07
- 2023-08
- 2023-08
- 2023-10
- 2020-09
- 2023-02
- 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
- 2025-07
- 2025-05
- 2025-06
- 2025-06
- 2025-01
- 2025-02
- 2023-10
- 2025-03
- 2025-03
- 2025-03
- 2025-03
- 2025-08
- 2025-03
- 2025-10
- 2025-09
-
Code Refactoring and Migration
-
Mutation Testing
-
Binary Analysis and Decompilation
-
Software Configuration
-
Log Analysis
-
Code Ranking
-
Software Modeling
-
-
6. Analysis of AI-Generated Code
-
AI-Generated Code Detection
-
Robustness
-
Others
-
Correctness
-
Security and Vulnerabilities
- 2024-11
- 2024-10
- 2024-06
- 2025-04
- 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
- 2025-08
- 2025-09
- 2024-10
- 2024-09
- 2024-09
- 2024-10
- 2024-10
- 2024-11
- 2024-12
- 2024-07
- 2024-11
- 2025-02
- 2025-05
- 2025-05
- 2025-06
- 2025-02
- 2025-02
- 2025-03
- 2025-03
- 2025-03
- 2025-07
- 2025-06
- 2024-08
- 2025-09
- 2025-10
- 2025-10
-
Efficiency
-
Hallucination
-
Bias
-
Interpretability
-
Privacy
-
API Usage
-
Contamination
-
-
7. Human-LLM Interaction
-
Others
- 2024-05
- 2024-05
- 2024-05
- 2024-06
- 2024-06
- 2025-04
- 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
- 2025-09
- 2024-05
- 2024-10
- 2024-10
- 2022-04
- 2024-09
- 2024-11
- 2025-02
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-05
- 2024-05
- 2024-06
- 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
- 2025-05
- 2025-10
-
-
8. Datasets
-
8.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [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
- [paper
- [paper
- [paper
- [paper
- [paper - codechanges)] |
- [paper
- [paper - group/mineSStuBs)] |
- [paper
- [paper - KTH/megadiff)] |
- [paper
- [paper - docstring-corpus)] |
- [paper
- [paper
- [paper
- [paper - group/diversevul)] |
- [paper - Targaryen/MC-Evaluation)] |
- [paper - 810A)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Coder/tree/main/qwencoder-eval/instruct/CodeArena)] |
- [paper
- 2025-04
- [paper
- [paper - level-Vulnerability-Detection)] |
- [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
- [paper - NLP/novicode)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- 2024-02
- [paper
- 2025-07 - Evaluation/MERA_CODE)]
- [paper - bench/SciCode)] |
- [paper
- 2025-07
- [paper
- [paper - team/coir)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- 2025-07 - perf/swe-perf)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-03
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2023-11
- 2024-08
- [paper
- [paper
- [paper
- [paper
- [paper - ai/WebApp1K-React)] |
- [paper - hu/TL-CodeSum)] |
- [paper
- [paper - kb/tree/main/MSR2019)] |
- [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 - 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 - jie-Huang/CoCoNote)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - V/HumanEval-V-Benchmark)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - group/TypeT5)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Research/CodeJudge-Eval)] |
- [paper - eval)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [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 - level-Vulnerability-Detection)] |
- 2024-04
- [paper
- [paper
- [paper - nlp/USACO)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - lily.github.io/sparc)] |
- [paper
- [paper
- [paper
- [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 - level-Vulnerability-Detection)] |
- [paper - codes/VulDeePecker)] |
- [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 - DK)] |
- [paper
- [paper - easel/StudentEval)] |
- [paper - SpiderCG)] |
- [paper - bench.github.io/)] |
- [paper - Code-Search-Evaluation-Dataset)] |
- [paper
- [paper
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper
- [paper
- [paper - LAB-SJTU/CosBench/wiki)] |
- [paper
- [paper
- [paper - Code/NL-code-search-WebQuery)] |
- [paper
- [paper
- 2024-11
- [paper - 0/commit0)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - weihan/SWE-QA-Bench)] |
- [paper - bench_Pro-os)] |
- [paper
- 2025-09
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - a-p/AetherCode)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - gmu/mHumanEval)] |
- [paper
- [paper
- [paper
- [paper - nl2sql)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - codegen/yabloco-benchmark)] |
- [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 - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - xl)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-02 - deepmind/bbeh)]
- 2025-02
- 2025-02
- 2025-02
- [paper - Bench-D65E/README.md)] |
- [paper - XL)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Bench)] |
- [paper - ai/geospatial-code-llms-dataset)] |
- [paper - AI4Code/CodeMMLU)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- 2024-10
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - Eval-Team/M2RC-Eval)] |
- [paper
- [paper - bench/JavaBench)] |
- 2024-06 - rag-bench/code-rag-bench)]
- 2024-06 - Research/lca-baselines)]
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2024-12 - eval)]
- [paper
- [paper - evolution-eval.github.io/)] |
- [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
- [paper - level-Vulnerability-Detection)] |
- [paper - liuzy/CodeUpdateArena)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - tan/CoCoNut-Artifact)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- 2025-02
- [paper - Benchmark)] |
- 2020-09
- 2023-02
- [paper - sudo/DependEval)] |
- [paper - TransEval)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - lab-code-research/MuST-CoST)] |
- 2025-02
- 2025-02
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - sri/TFix)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Pro/CodeEval-Pro/tree/main)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-01
- [paper - Bench)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - code-search)] |
- 2025-01
- 2025-06
- [paper - Hunyuan/ArtifactsBenchmark)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Eval-Official/CoreCodeBench)] |
- 2025-06
- 2025-05
- 2025-05
- 2025-05
- [paper - bench/oss-bench)] |
- [paper
- [paper
- [paper - github/Flow2Code)] |
- [paper - bench.github.io/)] |
- [paper
- [paper
- [paper - CARD/biomedsql)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - TrustEval-C)] |
- [paper - Dev)] |
- [paper - rebench)] |
- [paper - Bench)] |
- [paper - Research/git-good-bench)] |
- [paper - bench.github.io)] |
- [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
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - EA6F/)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - 7B74/README.md)] |
- [paper - 9/probench)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - swe-bench/multi-swe-bench)] |
- [paper - science/SWE-PolyBench)] |
- 2025-04
- [paper - level-benchmark-dataset-B132/README.md)] |
- [paper
- [paper - bench)] |
- [paper - level-Vulnerability-Detection)] |
- [paper - Pro)] |
- [paper - s-Last-Code-Exam/HLCE)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Ren/OJBench)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-10
- [paper
- [paper - ai/Falcon)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - us/download/103554)] |
- [paper
- [paper - eval)] |
- [paper
- [paper
- [paper
- [paper - data/)] |
- [paper - lily.github.io/spider)] |
- [paper - Lab/BookSQL)] |
- [paper - ai/Spider2)] |
- [paper
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Replication)] |
- [paper - JPG/VCode)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - Sharp-Bench)] |
- [paper
- [paper
- 2025-10
- 2025-10
- [paper
- [paper - interact.github.io/)] |
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- 2025-10
- [paper
- [paper
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
-
8.1 Pretraining
-
-
4. Datasets
-
4.2 Benchmarks
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper - level-Vulnerability-Detection)] |
- [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)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
- [paper
- [paper - level-Vulnerability-Detection)] |
-
-
2. Models
-
2.5 Reinforcement Learning on Code
- 2024-11
- 2023-10
- 2022-03
- 2023-01 - lab-code-research/PPOCoder)]
- 2022-07
- 2023-07 - scut/RLTF)]
- 2024-04
- 2024-02
- 2024-09
- 2025-02
- 2024-11
- 2025-08
- 2025-08
- 2025-09
- 2025-09
- 2024-10
- 2024-10
- 2024-01
- 2025-02
- 2024-09
- 2024-10
- 2024-10
- 2024-10
- 2024-10
- 2024-06
- 2024-06
- 2025-11
- 2025-06
- 2025-06
- 2025-05
- 2025-05
- 2025-02
- 2025-02
- 2025-06
- 2025-10
- 2025-10
- 2025-09
- 2025-10
-
2.2 Existing LLM Adapted to Code
-
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
- 2024-11
- 2025-03
- 2023-12
- 2024-01
- 2024-03
- 2024-09
- 2025-09
- 2025-09
- 2025-09
- 2025-09
- 2025-03
- 2024-12
- 2025-02
- 2025-02
- 2025-02
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- 2024-10
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2.1 Base LLMs and Pretraining Strategies
- 2024-05 - 7B)]
- 2025-01
- 2024-06
- 2022-04 - neox)]
- 2023-03
- 2023-09 - 1_5)]
- 2023-09 - inc/Baichuan2)]
- 2023-09
- 2024-01 - of-experts/)]
- 2024-01
- 2024-04 - ai/JetMoE)]
- 2024-06
- 2024-11
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- 2024-07
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- 2024-07
- 2024-04
- 2024-04
- 2024-05 - ai/DeepSeek-V2)]
- 2024-04 - FLM)]
- 2024-04 - llama/llama3)] [[paper](https://arxiv.org/abs/2407.21783)]
- 2024-07
- 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 - 3-family)]
- 2024-03 - ai/Yi)]
- 2025-09
- 2025-02
- 2025-03
- 2024-12
- 2024-12
- 2025-02
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- 2024-10
- 2025-05
- 2024-05 - art-projection/MAP-NEO)]
- 2024-10
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2.3 General Pretraining on Code
- 2020-05
- 2019-12 - research/google-research/tree/master/cubert)]
- 2020-02
- 2020-09
- 2021-08
- 2021-10
- 2021-12
- 2022-05
- 2022-02 - LMs)]
- 2022-03
- 2022-04
- 2020-10
- 2022-06
- 2022-07
- 2023-01
- 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)]
- 2025-09
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-
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)] |
-
-
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 |
- Self-Instruct: Aligning Language Models with Self-Generated Instructions - generated data |
- 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
- 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
- Training Compute-Optimal Large Language Models
- Large Language Models are Zero-Shot Reasoners
- 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) |
- RoFormer: Enhanced Transformer with Rotary Position Embedding
- 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).
- PaLM: Scaling Language Modeling with Pathways
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
-
-
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
1,188
8. Datasets
533
3. When Coding Meets Reasoning
284
2. Models
260
6. Analysis of AI-Generated Code
232
4. Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages
104
7. Human-LLM Interaction
70
News
40
9. Recommended Readings
32
5. Datasets
29
4. Datasets
20
1. Surveys
17
6. Datasets
4
Star History
2
7. User-LLM Interaction
1
Sub Categories
8.2 Benchmarks
560
3.5 Frontend Navigation
159
Text-To-SQL
157
Vulnerability Detection
116
Others
106
3.3 Code Agents
91
2.1 Base LLMs and Pretraining Strategies
90
Code Generation
85
Code Commenting and Summarization
83
Test Generation
78
Malicious Code Detection
75
Program Repair
74
2.4 (Instruction) Fine-Tuning on Code
69
3.1 Coding for Reasoning
66
3.4 Interactive Coding
55
Security and Vulnerabilities
55
2.3 General Pretraining on Code
50
Code Translation
45
Code Review
43
Frontend Development
42
Repository-Level Coding
38
2.5 Reinforcement Learning on Code
38
Code Similarity and Embedding (Clone Detection, Code Search)
35
Correctness
33
5.2 Benchmarks
30
Requirement Engineering
28
Program Proof
26
Log Analysis
25
AI-Generated Code Detection
25
Automated Machine Learning
24
Compiler Optimization
23
Binary Analysis and Decompilation
23
Code Refactoring and Migration
23
Code RAG
23
Issue Resolution
20
4.2 Benchmarks
20
Efficiency
19
3.2 Code Simulation
17
Software Configuration
16
Code QA & Reasoning
16
Robustness
15
Oracle Generation
15
Code Ranking
14
2.2 Existing LLM Adapted to Code
13
Fuzz Testing
12
Hallucination
12
Interpretability
11
API Usage
10
Software Modeling
10
Commit Message Generation
8
Bias
7
Mutation Testing
7
Privacy
7
8.1 Pretraining
6
Type Prediction
4
6.2 Benchmarks
4
Contamination
3