Text-to-Software Code Model

A Text-to-Software Code Model is a software programming-focused LLM that is a text-to-structured data model that accepts code generation prompts and produces software source code.

• AKA: Code Generation LLM, Text-to-Code Model, Natural Language to Code Model.
• Context:
  • It can enable Software Code Generation through natural language instructions and code context understanding.
  • It can process Code Generation Prompts through instruction parsing and intent understanding.
  • It can produce Software Source Code through code completion and syntax validation.
  • It can perform Code Debugging through error detection and solution suggestions.
  • It can support Code Documentation through comment generation and documentation synthesis.
  • ...
  • It can often handle Multi-Language Programming through language-specific tokens and cross-language translation.
  • It can often maintain Code Quality through static analysis and runtime verification.
  • It can often facilitate Interactive Programming through notebook environments and real-time feedback.
  • It can often support API Integration through API invocation and interface understanding.
  • It can often enable Position-Specific Generation through fill-in-middle capability and context-aware insertion.
  • ...
  • It can range from being a Small Code Parameter Model to being a Large Code Parameter Model, depending on its model scale (7B to 34B parameters).
  • It can range from being a Single Language Specialist to being a Multilingual Code Generator, depending on its language support scope.
  • It can range from being a Basic Code Assistant to being an Advanced Development System, depending on its functionality level.
  • It can range from being a Task-Specific Model to being a General Code Model, depending on its application domain.
  • ...
  • It can integrate with Development Environments for code suggestions.
  • It can connect to Code Repository Systems for context analysis.
  • It can support API Documentation Systems for interface generation.
  • It can utilize Code Testing Frameworks for solution validation.
  • ...
• Examples:
  • Commercial Code Models, such as:
    • Enterprise Solutions, such as:
      • OpenAI Codex for github copilot integration.
      • DeepMind AlphaCode for competitive programming.
    • Cloud Services, such as:
      • Amazon CodeWhisperer for aws development.
      • Google PaLM-Coder for cloud development.
  • Open Source Code Models, such as:
    • General Purpose Code Generators, such as:
      • Code Llama for multi-scale generation.
      • StarCoder for repository understanding.
    • Specialized Code Generators, such as:
      • CodeGeeX for multilingual support.
      • JuPyT5 for notebook programming.
  • Language-Specific Models, such as:
    • Python Specialists, such as:
      • PyCodeGPT for python generation.
      • CodeLLaMA-Python for python optimization.
    • Multi-Language Systems, such as:
      • PolyCoder for cross-language support.
      • ERNIE-Code for multilingual modeling.
  • Task-Specific Models, such as:
    • API Integration Models, such as:
      • APICoder for api implementation.
      • DocCoder for documentation generation.
    • Position-Aware Models, such as:
      • InCoder for code insertion.
      • FIM Model for middle completion.
  • ...
• Counter-Examples:
  • Text-to-JSON Models, which focus on data structure generation.
  • Text-to-Text Models, which produce natural language output.
  • Code-to-Text Models, which generate code documentation.
  • Text-to-Image Models, which create visual content.
  • General Language Models, which lack code-specific optimization.
• See: Software Code Generation System, Programming Language Model, Code Generation Framework, Software Development Assistant, Code Quality Validation System, API Integration Framework, Multi-Language Code System, Interactive Programming Environment.

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References

2023

• GBard
  • [[Text-to-software code LLMs (large language models)]] are a type of artificial intelligence (AI) that can generate code from natural language descriptions. They are trained on massive datasets of code and text, and they learn to identify the patterns and relationships between the two. This allows them to translate natural language descriptions of code into actual code in a variety of programming languages.

2023

• (Shen et al., 2023) ⇒ Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, Yuenan Guo, and Qianxiang Wang. (2023). “PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback.” doi:10.48550/arXiv.2307.14936
  • QUOTE: ...
    • Large Language Model for Code (Code LLMs): As a momentous milestone, Codex Chen et al. (2021) boasting a 12-billion-parameters model demonstrates the extraordinary capability to tackle up to 72% of Python programming problems. Subsequently, a new wave of code generation models, such as AlphaCode Li et al. (2022), PaLM-Coder Chowdhery et al. (2022), and PanGu-Coder Christopoulou et al. (2022), also were proposed. Despite the remarkable prowess exhibited by the aforementioned models, it is disheartening to note their unavailability as open-source projects. Therefore, several open-source code generation models, including CodeParrot [[Huggingface (2021)], PolyCoder Xu et al. (2022), PyCodeGPT Zan et al. (2022a), SantaCoder Allal et al. (2023), and StarCoder Li et al. (2023), were released, injecting fresh vigor into the realm of code generation Chen et al. (2022). Meanwhile, code generation models have also been applied to a broader range of practical coding scenarios. For example, CodeGeeX Zheng et al. (2023), BLOOM Scao et al. (2022) and ERNIE-Code Chai et al. (2022) have been proposed to facilitate multilingual modeling; JuPyT5 Chandel et al. (2022) is trained on a large corpus of Jupyter notebooks, aiming to elevate the experience of interactive programming; DocCoder Zhou et al. (2023a) and APICoder Zan et al. (2022b) have been proposed to empower language models with the ability to invoke APIs; Some models such as InCoder Fried et al. (2023), FIM Bavarian et al. (2022), MIM Nguyen et al. (2023), SantaCoder Allal et al. (2023), and StarCoder Li et al. (2023) support the code generation at arbitrary positions.

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