Methods › Natural Language Processing › Language Models › CodeGen

CodeGen

25 papers tagged archive 2025-07-28

Introduced by Erik Nijkamp et al. in CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

CodeGen is an autoregressive transformers with next-token prediction language modeling as the learning objective trained on a natural language corpus and programming language data curated from GitHub.

PaperSource

Papers archive 2025-07-28

25 shown of 25, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 25 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Code Generation19
HumanEval6
Language Modelling5
Program Synthesis4
mbpp4
Code Completion3
Contrastive Learning3
Language Modeling3
Benchmarking2
Data Augmentation2
In-Context Learning2
Large Language Model2
Memorization2
Attribute1
Code Repair1
Code Summarization1
Model Selection1
Program Repair1
Prompt Engineering1
Question Answering1

Usage over time archive 2025-07-28

Papers per year tagged with CodeGen: 2022 to 2025, peak 12 12 0 2022: 7 papers 2022 2023: 12 papers 2023 2024: 5 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (25 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Language Models

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