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/suzgunmirac/meta-prompting
https://github.com/swe-bench/swe-bench
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
https://github.com/tamnil/esp32-xtensa-study
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/tatsu-lab/stanford_alpaca
https://github.com/teelinsan/camoscio
https://github.com/terrierteam/pyterrier_rag
https://github.com/teticio/llama-squad
https://github.com/thudm/codegeex2
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
https://github.com/tihonovcore/model
https://github.com/tiwarikajal/Seq2SQL-
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/tmelyralab/musev
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
https://github.com/transm2/mmf3
https://github.com/transms/m2ts
https://github.com/tuyentv2009/Face-Recognition-
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/usyd-fsalab/fp6_llm
https://github.com/valdecy/pybibx
https://github.com/vcskaushik/LLMzip
https://github.com/vhellendoorn/code-lms
https://github.com/vxcontrol/pentagi
https://github.com/wangrongsheng/caregpt
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
https://github.com/wanjinchang/MTCNN_USE_TF_E2E
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/wronnyhuang/SQLNet_inference
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/xiaojunxu/SQLNet
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/xiaoman-zhang/PMC-VQA
https://github.com/xiye17/torchASN
Abstract Syntax Networks for Code Generation and Semantic Parsing
https://github.com/xlang-ai/ds-1000
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
https://github.com/xqx12/daily-info
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
https://github.com/xuetianci/pacit
https://github.com/xusenlinzy/api-for-open-llm
https://github.com/xverse-ai/xverse-13b
https://github.com/xvyaward/owq
https://github.com/xzhang97666/alpacare
https://github.com/yangkai2g7k/nl2code
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/yangyucheng000/University/tree/main/model-1/plbart
Unified Pre-training for Program Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.