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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/giclab/transfer-tuning

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Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation

https://github.com/githubnext/followup-attention

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Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration

https://github.com/gladcolor/llm-geo

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Autonomous GIS: the next-generation AI-powered GIS

https://github.com/going-doer/paper2code

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Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

https://github.com/gonglinyuan/ast_t5

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AST-T5: Structure-Aware Pretraining for Code Generation and Understanding

https://github.com/google-research/babelcode

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Measuring The Impact Of Programming Language Distribution

https://github.com/grill-lab/oat

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GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants

https://github.com/grill-lab/wote

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GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants

https://github.com/guosyjlu/ds-agent

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DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

https://github.com/guotong1988/NL2SQL

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Bidirectional Attention for SQL Generation

https://github.com/guotong1988/NL2SQL-BERT

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Content Enhanced BERT-based Text-to-SQL Generation

https://github.com/guotong1988/NL2SQL-RULE

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Content Enhanced BERT-based Text-to-SQL Generation

https://github.com/haitaowutju/turing-machine-bench

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Computational Reasoning of Large Language Models

https://github.com/hajipour/simscood

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SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models

https://github.com/hallerpatrick/pecc

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PECC: Problem Extraction and Coding Challenges

https://github.com/hamedtaherkhani/epic

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EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation

https://github.com/haokang-timmy/latencysensitivebench

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Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs

https://github.com/haotian-liu/LLaVA

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Unified Modeling Language Code Generation from Diagram Images Using Multimodal Large Language Models

https://github.com/happylkx/mmcode

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MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems

https://github.com/hendrycks/apps

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Measuring Coding Challenge Competence With APPS

https://github.com/hexacoder-ai/hexacoder

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HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data

https://github.com/hkbunlp/scratcheval

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ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges

https://github.com/hkunlp/icl-ceil

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Compositional Exemplars for In-context Learning

https://github.com/hkunlp/icl-selective-annotation

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Selective Annotation Makes Language Models Better Few-Shot Learners

https://github.com/hkunlp/symgen

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Generating Data for Symbolic Language with Large Language Models

https://github.com/hkust-nlp/codeio

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CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction

https://github.com/hltchkust/chatgpt-evaluation

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A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

https://github.com/hongcheki/sweet-watermark

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Who Wrote this Code? Watermarking for Code Generation

https://github.com/horseee/dkv-cache

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dKV-Cache: The Cache for Diffusion Language Models

https://github.com/hpai-bsc/turtle

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TuRTLe: A Unified Evaluation of LLMs for RTL Generation

https://github.com/hpc-fortran2cpp/fortran2cpp

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Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration

https://github.com/huangd1999/AgentCoder

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AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

https://github.com/huangd1999/EffiBench

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EffiBench: Benchmarking the Efficiency of Automatically Generated Code

https://github.com/huangd1999/effi-code

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EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

https://github.com/huangd1999/efficoder

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EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning

https://github.com/huangd1999/effilearner

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EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization

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