NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917673869246464 |
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| author | Akl, Hanna Abi |
| author_facet | Akl, Hanna Abi |
| contents | We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, achieves a Macro-F1 score of 91.412% with an increase of 1.033% after data augmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16910 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification Akl, Hanna Abi Software Engineering Artificial Intelligence We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, achieves a Macro-F1 score of 91.412% with an increase of 1.033% after data augmentation. |
| title | NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2402.16910 |