Redefining Developer Assistance: Through Large Language Models in Software Ecosystem
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929278656970752 |
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| author | Banerjee, Somnath Dutta, Avik Layek, Sayan Sahoo, Amruit Joyce, Sam Conrad Hazra, Rima |
| author_facet | Banerjee, Somnath Dutta, Avik Layek, Sayan Sahoo, Amruit Joyce, Sam Conrad Hazra, Rima |
| contents | In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama, a model developed through instruction tuning, to assist developers in processing software-related natural language queries. This model, a variant of instruction tuned LLM, is particularly adept at handling intricate technical documentation, enhancing developer capability in software specific tasks. The creation of DevAssistLlama involved constructing an extensive instruction dataset from various software systems, enabling effective handling of Named Entity Recognition (NER), Relation Extraction (RE), and Link Prediction (LP). Our results demonstrate DevAssistLlama's superior capabilities in these tasks, in comparison with other models including ChatGPT. This research not only highlights the potential of specialized LLMs in software development also the pioneer LLM for this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05626 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Redefining Developer Assistance: Through Large Language Models in Software Ecosystem Banerjee, Somnath Dutta, Avik Layek, Sayan Sahoo, Amruit Joyce, Sam Conrad Hazra, Rima Software Engineering Artificial Intelligence In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama, a model developed through instruction tuning, to assist developers in processing software-related natural language queries. This model, a variant of instruction tuned LLM, is particularly adept at handling intricate technical documentation, enhancing developer capability in software specific tasks. The creation of DevAssistLlama involved constructing an extensive instruction dataset from various software systems, enabling effective handling of Named Entity Recognition (NER), Relation Extraction (RE), and Link Prediction (LP). Our results demonstrate DevAssistLlama's superior capabilities in these tasks, in comparison with other models including ChatGPT. This research not only highlights the potential of specialized LLMs in software development also the pioneer LLM for this domain. |
| title | Redefining Developer Assistance: Through Large Language Models in Software Ecosystem |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2312.05626 |