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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models
Planning-Driven Programming: A Large Language Model Programming Workflow
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation
Generating Unseen Code Tests In Infinitum
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Can Large Language Models Solve Robot Routing?
Text-to-Code Generation with Modality-relative Pre-training
Magicoder: Empowering Code Generation with OSS-Instruct
Guiding Language Models of Code with Global Context using Monitors
Code Execution with Pre-trained Language Models
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
StructCoder: Structure-Aware Transformer for Code Generation
Compilable Neural Code Generation with Compiler Feedback
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
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