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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/chenjshnn/Object-Detection-for-Graphical-User-Interface

공식 코드

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

https://github.com/chiyeunglaw/amr-evol

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AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation

https://github.com/chiyusong/dynamics-of-instruction-tuning

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Dynamics of Instruction Tuning: Each Ability of Large Language Models Has Its Own Growth Pace

https://github.com/chrismun/vvllm

공식 코드

LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation

https://github.com/co1lin/cweval

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CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation

https://github.com/code-rag-bench/code-rag-bench

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CodeRAG-Bench: Can Retrieval Augment Code Generation?

https://github.com/codeeff/ecco

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ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?

https://github.com/codellm-research/codejudge-eval

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CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?

https://github.com/codium-ai/alphacodium

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Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering

https://github.com/cognitiveailab/bytesized32

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ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games

https://github.com/cognitiveailab/matter-of-fact

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Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science

https://github.com/cognitiveailab/words2wires

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From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions

https://github.com/commit-0/commit0

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Commit0: Library Generation from Scratch

https://github.com/cone-mt/benchmax

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BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models

https://github.com/constructive-codes/cpro1

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Using Code Generation to Solve Open Instances of Combinatorial Design Problems

https://github.com/cornell-zhang/unisparse

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UniSparse: An Intermediate Language for General Sparse Format Customization

https://github.com/cosmaadrian/rocode

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RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian

https://github.com/coulsonlee/dy-dca-eccv2024

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Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design

https://github.com/csbench/csbench

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CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

https://github.com/csplevuanh/quant_anomaly

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RN-F: A Novel Approach for Mitigating Contaminated Data in Large Language Models

https://github.com/cuhk-arise/mas-resilience

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On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents

https://github.com/cuhk-shenzhen-se/utboost

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UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench

https://github.com/cvlab-columbia/viper

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ViperGPT: Visual Inference via Python Execution for Reasoning

https://github.com/dakuo/haconvgnn

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HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks

https://github.com/damo-nlp-sg/gpt4-as-dataanalyst

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Is GPT-4 a Good Data Analyst?

https://github.com/daniel442li/gpt-human-eval

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HumanEval on Latest GPT Models -- 2024

https://github.com/dasepli/codeie

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CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors

https://github.com/declare-lab/llm-reasoningtest

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Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions

https://github.com/deep-spin/doce

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DOCE: Finding the Sweet Spot for Execution-Based Code Generation

https://github.com/deeplearnxmu/unigencoder

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UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation

https://github.com/deepmind/card2code

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Latent Predictor Networks for Code Generation

https://github.com/deepmind/code_contests

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Competition-Level Code Generation with AlphaCode

https://github.com/deepseek-ai/DeepSeek-Coder

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DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

https://github.com/deepsoftwareanalytics/domaincodebench

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Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark

https://github.com/deepsoftwareanalytics/llmcodinghallucination

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LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation

https://github.com/deepsoftwareanalytics/swe-factory

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SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks

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