Summary SoTaNa The Open-Source Software Development Assistant arxiv.org
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SoTaNa is an open-source software development assistant that utilizes ChatGPT and fine-tuning to help developers with data and code summarization.
Slides
Slide Presentation (9 slides)
Key Points
- SoTaNa is an open-source software development assistant that utilizes ChatGPT and enhances the LLaMA model.
- SoTaNa demonstrates effectiveness in assisting developers through human evaluation and has capabilities in code summarization and generation.
- OpenAI has curated instruct-based datasets to address the challenge of understanding human-written instructions.
- The approach focuses on parameter-efficient tuning of large language models (LLMs) using the Lora method.
- SoTaNa leverages LLMs to generate high-quality instruction-based data for software engineering tasks and fine-tunes the LLaMA model with software engineering-related data.
Summaries
21 word summary
SoTaNa is an open-source software development assistant that uses ChatGPT and fine-tuning to assist developers with instruction-based data and code summarization.
38 word summary
SoTaNa is an open-source software development assistant that utilizes ChatGPT to generate instruction-based data for software engineering tasks and enhances the LLaMA model through fine-tuning. It demonstrates effectiveness in assisting developers and highlights capabilities in code summarization and
390 word summary
SoTaNa is an open-source software development assistant that utilizes ChatGPT to generate high-quality instruction-based data for software engineering tasks and enhances the open-source foundation model LLaMA through parameter-efficient fine-tuning. The objective of SoTaNa is
The document discusses the development of SoTaNa, an open-source software development assistant based on a large language model. The model's effectiveness in assisting developers is demonstrated through human evaluation. The paper also highlights the model's capabilities in code summarization and generation
OpenAI has developed models that convert NLP tasks into a unified format and use multi-task learning to achieve good results on new tasks. However, understanding human-written instructions remains challenging for these models. OpenAI has curated instruct-based datasets to address this challenge
Our approach focuses on parameter-efficient tuning of large language models (LLMs) using the Lora method. Lora freezes pre-trained model parameters and introduces trainable low-rank decomposition matrices into each Transformer layer. We evaluate the model's ability to understand code
The Open-Source Software Development Assistant, SoTaNa, utilizes Gaussian initialization for matrix A and sets matrix B to zero. The statistics of SoTaNa, including training times, are shown in Table 1. SoTaNa is evaluated through human
The best way to get a file extension in PHP is to use the pathinfo() function. This function returns an array containing the file name, extension, path, and other information about the file. Another option is to use the explode() function,
To find a file extension in PHP, there are several methods suggested. One method is to split the file name with a delimiter and retrieve the last part. Another method is to use the 'pathinfo()' function, which returns an array that includes the
SoTaNa is an open-source software development assistant that leverages Large Language Models (LLMs) to generate high-quality instruction-based data for software engineering tasks. It fine-tunes the LLaMA model with software engineering-related data to enhance its capabilities
This excerpt includes references to various research papers and projects related to open-source software development and language models. It mentions studies on the impact of instruction data scaling on large language models, the development of instruction-following language models for code generation, and investigations into
This excerpt contains a list of references to various papers and models related to open-source software development and language models. It includes references to models such as Alpaca, Chatgpt, Llama, and Wizardlm, as well as papers on evaluation
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