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/consequentai/fneval
https://github.com/cooldawnant/infochartqa
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
https://github.com/craftjarvis/rat
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
https://github.com/csebuetnlp/CoDesc
https://github.com/cybermetric/cybermetric
How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models
https://github.com/cydonia999/VGGFace2-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/dashweta/MachineLearningPython
pix2code: Generating Code from a Graphical User Interface Screenshot
https://github.com/davidgolub/QuestionGeneration
https://github.com/deepsoftwareanalytics/omnigirl
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
https://github.com/dengpingfan/fsgan
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/devanshbatra04/Named-Entity-Language-Models
https://github.com/dheerajsingh7351/facedetect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/ecnu-icalk/educhat
https://github.com/ecolab-postech/owq
https://github.com/eladlev/autoprompt
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/espressif/esp-who
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/eternityyw/tram-benchmark
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/rlhf_trojan_competition
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/facebookresearch/TransCoder
https://github.com/facebookresearch/chai
https://github.com/facebookresearch/fairseq2
https://github.com/facico/chinese-vicuna
https://github.com/fajri91/indommlu
https://github.com/fanqiwan/fuseai
https://github.com/fewshotcdcs/cdcs
https://github.com/flagalpha/llama2-chinese
https://github.com/foundation-model-stack/fms-fsdp
https://github.com/frabbisw/robustextended
https://github.com/freedomintelligence/huatuogpt
https://github.com/freedomintelligence/sdak
https://github.com/fsoft-ai4code/codecapybara
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.