Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach

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Udgivet i:arXiv.org (Dec 1, 2024), p. n/a
Hovedforfatter: Chakrabarty, Sayak
Andre forfattere: Pal, Souradip
Udgivet:
Cornell University Library, arXiv.org
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Online adgang:Citation/Abstract
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022 |a 2331-8422 
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045 0 |b d20241201 
100 1 |a Chakrabarty, Sayak 
245 1 |a Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach 
260 |b Cornell University Library, arXiv.org  |c Dec 1, 2024 
513 |a Working Paper 
520 3 |a Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several papers have been written on generating documentation for source code using neural network architectures. With the recent advancements in LLM technology, some open-source applications have been developed to address this problem. However, these applications typically rely on the OpenAI APIs, which incur substantial financial costs, particularly for large repositories. Moreover, none of these open-source applications offer a fine-tuned model or features to enable users to fine-tune. Additionally, finding suitable data for fine-tuning is often challenging. Our application addresses these issues which is available at https://pypi.org/project/readme-ready/. 
653 |a Repositories 
653 |a Source code 
653 |a Documentation 
653 |a Neural networks 
653 |a Large language models 
653 |a Open source software 
700 1 |a Pal, Souradip 
773 0 |t arXiv.org  |g (Dec 1, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3138989510/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.00726