Text-to-Software Code Model

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

• Context:
  • It can be used by an Text-to-Software Generation System (that solves text-to-software generation task).
  • It can offer functionalities like code completion, debugging, and generating code from descriptions.
  • It can support multiple programming languages, including Python, Java, C++, and others.
  • It can be used in various software engineering contexts, ranging from professional development to educational settings.
  • It can have specialized variations, such as CodeLLaMa - Python for Python-specific tasks, and CodeLLaMa - Instruct for enhanced natural language instruction following.
  • It can be available in different sizes, such as 7B, 13B, and 34B, to cater to different computational and latency needs.
  • It can be especially useful in scenarios requiring understanding of large codebases or complex programming concepts.
  • ...
• Example(s):
  • OpenAI Codex.
  • PanGu-Coder2.
  • DeepMind AlphaCode.
  • Code Llama.
  • ...
• Counter-Example(s):
  • Text-to-JSON Model.
  • Text-to-Text Model.
  • Code-to-Text Model.
  • Text-to-Image Model.
  • General Language Model.
• See: CodeGen Model.

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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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