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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/mbzuai-llm/web2code

공식 코드

Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs

https://github.com/mccodeai/mccoder

공식 코드

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

https://github.com/mcgill-nlp/incontext-code-generation

공식 코드

Evaluating In-Context Learning of Libraries for Code Generation

https://github.com/md-ashraful-pramanik/mapcoder

공식 코드

MapCoder: Multi-Agent Code Generation for Competitive Problem Solving

https://github.com/mdrafiqulrabin/CI-Memorization

공식 코드

Memorization and Generalization in Neural Code Intelligence Models

https://github.com/metacopilot/dseval

공식 코드

Benchmarking Data Science Agents

https://github.com/metauto-ai/agent-as-a-judge

공식 코드

Agent-as-a-Judge: Evaluate Agents with Agents

https://github.com/michiyasunaga/DrRepair

공식 코드

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

https://github.com/microsoft/CoML

공식 코드

MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks

https://github.com/microsoft/CodeBERT

공식 코드

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

https://github.com/microsoft/CodeXGLUE

공식 코드

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

https://github.com/microsoft/DeepSpeed

공식 코드

ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks

https://github.com/microsoft/ProphetNet

공식 코드

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

https://github.com/microsoft/PythonProgrammingPuzzles

공식 코드

Programming Puzzles

https://github.com/microsoft/coderanker

공식 코드

Fault-Aware Neural Code Rankers

https://github.com/microsoft/codet

공식 코드

CodeT: Code Generation with Generated Tests

https://github.com/microsoft/data-formulator

공식 코드

Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way

https://github.com/microsoft/monitors4codegen

공식 코드

Guiding Language Models of Code with Global Context using Monitors

https://github.com/microsoft/pycodegpt

공식 코드

CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation

https://github.com/microsoft/qlib

공식 코드

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization

https://github.com/microsoft/rd-agent

공식 코드

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization

https://github.com/microsoft/stop

공식 코드

Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

https://github.com/microsoft/wavecoder

공식 코드

WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning

https://github.com/miho-koda/locationreasoner

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LocationReasoner: Evaluating LLMs on Real-World Site Selection Reasoning

https://github.com/mikivishy/fullfront

공식 코드

FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow

https://github.com/mindhunter01/rmoa

공식 코드

RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation

https://github.com/mistralai/mistral-src

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https://github.com/mitdbg/kramabench

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KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes

https://github.com/mkhfring/largelanguagemodels

공식 코드

Investigating the Efficacy of Large Language Models for Code Clone Detection

https://github.com/mobilellm/autodroid-v2

공식 코드

AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation

https://github.com/mobilellm/chainstream

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ChainStream: An LLM-based Framework for Unified Synthetic Sensing

https://github.com/modelscope/modelscope

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VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

https://github.com/moment-timeseries-foundation-model/timeseriesgym

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TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents

https://github.com/morning9393/etpo

공식 코드

Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement

https://github.com/mpi-softsec/difftrust

공식 코드

Estimating Correctness Without Oracles in LLM-Based Code Generation

https://github.com/mraihan-gmu/mHumanEval-Benchmark

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mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation

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