https://github.com/Nickolas-option/QA_on_Tabular_Data_SemEval2025_Task8
공식 코드Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
Papers with Code 아카이브
Text-to-Code Generation
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Text-to-Code Generation is a task where we can generate code based on the natural language description.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Zhehui-Huang/LLM_Routing
공식 코드https://github.com/ise-uiuc/magicoder
공식 코드https://github.com/microsoft/CodeBERT
공식 코드https://github.com/microsoft/CodeXGLUE
공식 코드CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/microsoft/monitors4codegen
공식 코드Guiding Language Models of Code with Global Context using Monitors
https://github.com/reddy-lab-code-research/structcoder
공식 코드StructCoder: Structure-Aware Transformer for Code Generation
https://github.com/salesforce/codet5
공식 코드https://github.com/wyt2000/InverseCoder
공식 코드InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
https://github.com/you68681/lpw
공식 코드Planning-Driven Programming: A Large Language Model Programming Workflow
https://github.com/Avmb/semantic_neq_game
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/MindSpore-paper-code-2/code399/tree/main/Pangu_alpha
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
https://github.com/awsm-research/vulrepair
https://github.com/deeplearnxmu/unigencoder
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/facebookresearch/CodeGen
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/fewshotcdcs/cdcs
https://github.com/jetbrains-research/commit_message_generation
https://github.com/kilimanj4r0/code-summarization-beyond-function-level
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/kyle-lyu/codeact
https://github.com/kyle-lyu/data-efficient-finetuning
https://github.com/salesforce/coderl
https://github.com/sberbank-ai/fusion_brain_aij2021
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
https://github.com/yueyuel/programgen-lms-reliability
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.