https://github.com/jorge-martinez-gil/graphcodebert-feature-integration
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
Papers with Code 아카이브
Code Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 28
- 보존된 평가 기록
- 241
Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.
Source: Deep Learning for Source Code Modeling and Generation
Image source: Measuring Coding Challenge Competence With APPS
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/judge0/judge0
ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?
https://github.com/juyongjiang/codeup
https://github.com/k4black/codebleu
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
https://github.com/kagnlp/CodeGenerator
https://github.com/kagnlp/Xolver
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
https://github.com/kamanphoebe/look-into-moes
https://github.com/kanishkg/synchromesh
Synchromesh: Reliable code generation from pre-trained language models
https://github.com/kasnerz/tabgenie
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/kayvr/token-hawk
https://github.com/kbressem/medalpaca
https://github.com/kevinleewy/pytorch_pix2code
pix2code: Generating Code from a Graphical User Interface Screenshot
https://github.com/kh-mo/QA_wikisql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/kilimanj4r0/code-summarization-beyond-function-level
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/knowlab/bi-weekly-paper-presentation
https://github.com/krafton-ai/korani
https://github.com/krantirk/Face-detection-and-counting-
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/kuaikuaikim/DFace
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/kyle-lyu/codeact
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
https://github.com/kzCassie/ucl_nlp
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
https://github.com/lastmile-ai/aiconfig
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
https://github.com/lbaf23/llm-cocoevo
CoCoEvo: Co-Evolution of Programs and Test Cases to Enhance Code Generation
https://github.com/lbaf23/testchain
Large Language Models as Test Case Generators: Performance Evaluation and Enhancement
https://github.com/leiluk1/codesearcher
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
https://github.com/lflage/openfactscore
https://github.com/liubest/HAConvGNN
https://github.com/llSourcell/SQL_Database_Optimization
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/llamafamily/llama-chinese
https://github.com/longhao-chen/aicas2024
https://github.com/lucidrains/CALM-pytorch
LLM Augmented LLMs: Expanding Capabilities through Composition
https://github.com/lucidrains/CoCa-pytorch
https://github.com/lucidrains/PaLM-jax
https://github.com/lucidrains/PaLM-pytorch
https://github.com/mariushobbhahn/swebench-verified-mini
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
https://github.com/matlab-deep-learning/mtcnn-face-detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/mcgill-nlp/length-generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.