[代码大模型]用于代码的大语言模型合集_代码大模型现在有多少个

原始LM

1.Lamda: Language models for dialog applications

https://arxiv.org/abs/2201.08239

2.Palm: Scaling language modeling with pathways

https://arxiv.org/abs/2204.02311

3.Gpt-neox-20b: An open-source autoregressive language model

https://arxiv.org/abs/2204.06745

https://github.com/EleutherAI/gpt-neox

4.BLOOM: A 176b-parameter open-access multilingual language model

https://arxiv.org/abs/2211.05100

5.lama: Open and efficient foundation language models

https://arxiv.org/abs/2302.13971

6.GPT-4 technical report

https://arxiv.org/abs/2303.08774

7.lama 2: Open foundation and finetuned chat models

https://arxiv.org/abs/2307.09288

8.Textbooks are all you need II: phi-1.5 technical report

https://arxiv.org/abs/2309.05463

适配LM

1.Evaluating large language models trained on code

https://arxiv.org/abs/2107.03374

2.Palm: Scaling language modeling with pathways

https://arxiv.org/abs/2204.02311

3.Solving quantitative reasoning problems with language models

https://arxiv.org/abs/2206.14858

4.Palm 2 technical report

https://arxiv.org/abs/2305.10403v3

5.Code llama: Open foundation models for code

https://arxiv.org/abs/2308.12950

专业LM

1. Learning and evaluating contextual embedding of source code

https://arxiv.org/abs/2001.00059

2.Codebert: A pre-trained model for programming and natural languages

https://arxiv.org/abs/2002.08155

3.Graphcodebert: Pre-training code representations with data flow

https://arxiv.org/abs/2009.08366

4.Syncobert: Syntax-guided multi-modal contrastive pre-training for code representation.

https://arxiv.org/abs/2108.04556

5.CODE-MVP: learning to represent source code from multiple views with contrastive pre-training

https://arxiv.org/abs/2205.02029

6.Intellicode compose: code generation using transformer

https://arxiv.org/abs/2005.08025

7.Codexglue: A machine learning benchmark dataset for code understanding and generation

https://arxiv.org/abs/2102.04664

8.A systematic evaluation of large language models of code

https://arxiv.org/abs/2202.13169

https://github.com/VHellendoorn/Code-LMs

9.Codegen: An open large language model for code with multi-turn program synthesis

https://arxiv.org/abs/2203.13474

https://github.com/salesforce/CodeGen

10.CERT: continual pretraining on sketches for library-oriented code generation

https://arxiv.org/abs/2206.06888

https://github.com/microsoft/PyCodeGPT

11.Pangu-coder: Program synthesis with function-level language modeling

https://arxiv.org/abs/2207.11280

12.Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x

https://arxiv.org/abs/2303.17568

https://github.com/THUDM/CodeGeeX

13.Textbooks are all you need

https://arxiv.org/abs/2306.11644

14.Codefuse-13b: A pretrained multi-lingual code large language model

https://arxiv.org/abs/2310.06266

15.Incoder: A generative model for code infilling and synthesis

https://arxiv.org/abs/2204.05999

https://sites.google.com/view/incoder-code-models

16.Santacoder: don’t reach for the stars!

https://arxiv.org/abs/2301.03988

https://huggingface.co/bigcode

17.Starcoder: may the source be with you!

https://arxiv.org/abs/2305.06161

18.Multi-task learning based pre-trained language model for code completion

https://arxiv.org/abs/2012.14631

19.Unixcoder: Unified cross-modal pre-training for code representation

https://arxiv.org/abs/2203.03850

20.Pymt5: multi-mode translation of natural language and python code with transformers

https://arxiv.org/abs/2010.03150

21.Studying the usage of text-to-text transfer transformer to support code-related tasks

https://arxiv.org/abs/2102.02017

22.DOBF: A deobfuscation pre-training objective for programming languages

https://arxiv.org/abs/2102.07492

23.Unified pre-training for program understanding and generation

https://arxiv.org/abs/2103.06333

24.Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation

https://arxiv.org/abs/2109.00859

25.Sptcode: Sequence-to-sequence pre-training for learning source code representations

https://arxiv.org/abs/2201.01549

26.Competition-level code generation with alphacode

https://arxiv.org/abs/2203.07814

27.Natgen: generative pre-training by "naturalizing" source code

https://arxiv.org/abs/2206.07585

28.Codet5+: Open code large language models for code understanding and generation

https://arxiv.org/abs/2305.07922

代码微调

1.Wizardcoder: Empowering code large language models with evolinstruct

https://arxiv.org/abs/2306.08568

https://github.com/nlpxucan/WizardLM

2.Pangu-coder2: Boosting large language models for code with ranking feedback

https://arxiv.org/abs/2307.14936

3.Octopack: Instruction tuning code large language models

https://arxiv.org/abs/2308.07124

https://github.com/bigcode-project/octopack

4.Mftcoder: Boosting code llms with multitask fine-tuning

https://arxiv.org/abs/2311.02303

https://github.com/codefuse-ai/MFTCOder

5.Compilable neural code generation with compiler feedback

https://arxiv.org/abs/2203.05132

6.Coderl: Mastering code generation through pretrained models and deep reinforcement learning

https://arxiv.org/abs/2207.01780

7.Execution-based code generation using deep reinforcement learning

https://arxiv.org/abs/2301.13816

8.RLTF: reinforcement learning from unit test feedback

https://arxiv.org/abs/2307.04349

https://github.com/Zyq-scut/RLTF