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Code Generation

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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/anirudhkhatry/crust-bench

공식 코드

CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation

https://github.com/anniedoris/cad-coder

공식 코드

CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation

https://github.com/anthropics/hh-rlhf

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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

https://github.com/apexlab/codeapex

공식 코드

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

https://github.com/apple/ml-diffucoder

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DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation

https://github.com/arise-lab/cycle_oopsla_24

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CYCLE: Learning to Self-Refine the Code Generation

https://github.com/arise-lab/semcoder

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SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning

https://github.com/arminmoin/ML-Quadrat

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From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)

https://github.com/arthurizijar/kb-coder

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Code-Style In-Context Learning for Knowledge-Based Question Answering

https://github.com/autogluon/autogluon-assistant

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MLZero: A Multi-Agent System for End-to-end Machine Learning Automation

https://github.com/avmb/inverse_scaling_prize_code_identifier_swap

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The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python

https://github.com/ayanami2003/gate

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GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks

https://github.com/b04901014/mqtts

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A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech

https://github.com/banksy23/xcoder

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How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data

https://github.com/beyonderxx/trace

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TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

https://github.com/bigcode-project/astraios

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Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models

https://github.com/bigcode-project/bigcode-evaluation-harness

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SantaCoder: don't reach for the stars!

https://github.com/bigcode-project/bigcodebench

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BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

https://github.com/bigcode-project/bigcodebench-annotation

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BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

https://github.com/bigcode-project/octopack

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OctoPack: Instruction Tuning Code Large Language Models

https://github.com/bigcode-project/selfcodealign

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SelfCodeAlign: Self-Alignment for Code Generation

https://github.com/bigcode-project/starcoder

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SantaCoder: don't reach for the stars!

https://github.com/bin123apple/autocoder

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AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}

https://github.com/bin123apple/fortran-cpp-hpc-code-translation-dataset

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Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++

https://github.com/boazlavon/eg_cfg

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Execution Guided Line-by-Line Code Generation

https://github.com/bonankou/attention-alignment-empirical-study

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Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?

https://github.com/borealisai/code-gen-tae

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Code Generation from Natural Language with Less Prior and More Monolingual Data

https://github.com/brown-palm/visual-question-decomposition

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Analyzing Modular Approaches for Visual Question Decomposition

https://github.com/buaa-clab/circuitmind

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Towards Optimal Circuit Generation: Multi-Agent Collaboration Meets Collective Intelligence

https://github.com/buaa-clab/reasoningv

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ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model

https://github.com/bytedance-seed/seed-coder

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Seed-Coder: Let the Code Model Curate Data for Itself

https://github.com/bytedance/Web-Bench

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Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks

https://github.com/caigaojiang/llmopt

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LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch

https://github.com/cbjuan/tokenizers-neural-nets-2020-paper

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Automated Source Code Generation and Auto-completion Using Deep Learning: Comparing and Discussing Current Language-Model-Related Approaches

https://github.com/chartgalaxy/chartgalaxy

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ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation

https://github.com/chartmimic/chartmimic

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ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

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