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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07408 |
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| _version_ | 1866916683043569664 |
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| author | Flanders, Samuel Nungsari, Melati Loong, Mark Cheong Wing |
| author_facet | Flanders, Samuel Nungsari, Melati Loong, Mark Cheong Wing |
| contents | This paper explores the use of large language models (LLMs), here represented by GPT 3.5-Turbo to perform coding for a thematic analysis. Coding is highly labor intensive, making it infeasible for most researchers to conduct exhaustive thematic analyses of large corpora. We utilize few-shot prompting with higher quality codes generated on semantically similar passages to enhance the quality of the codes while utilizing a cheap, more easily scalable model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Coding with Few-Shot Prompting for Thematic Analysis Flanders, Samuel Nungsari, Melati Loong, Mark Cheong Wing Computation and Language This paper explores the use of large language models (LLMs), here represented by GPT 3.5-Turbo to perform coding for a thematic analysis. Coding is highly labor intensive, making it infeasible for most researchers to conduct exhaustive thematic analyses of large corpora. We utilize few-shot prompting with higher quality codes generated on semantically similar passages to enhance the quality of the codes while utilizing a cheap, more easily scalable model. |
| title | AI Coding with Few-Shot Prompting for Thematic Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.07408 |