Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases
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arXiv
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| Format: | Preprint |
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2025
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| author | Mitra, Modhurita de Vos, Martine G. Cortinovis, Nicola Ometto, Dawa |
| author_facet | Mitra, Modhurita de Vos, Martine G. Cortinovis, Nicola Ometto, Dawa |
| contents | There has been enormous interest in generative AI since ChatGPT was launched in 2022. However, there are concerns about the accuracy and consistency of the outputs of generative AI. We have carried out an exploratory study on the application of this new technology in research data processing. We identified tasks for which rule-based or traditional machine learning approaches were difficult to apply, and then performed these tasks using generative AI.
We demonstrate the feasibility of using the generative AI model Claude 3 Opus in three research projects involving complex data processing tasks:
1) Information extraction: We extract plant species names from historical seedlists (catalogues of seeds) published by botanical gardens.
2) Natural language understanding: We extract certain data points (name of drug, name of health indication, relative effectiveness, cost-effectiveness, etc.) from documents published by Health Technology Assessment organisations in the EU.
3) Text classification: We assign industry codes to projects on the crowdfunding website Kickstarter.
We share the lessons we learnt from these use cases: How to determine if generative AI is an appropriate tool for a given data processing task, and if so, how to maximise the accuracy and consistency of the results obtained. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases Mitra, Modhurita de Vos, Martine G. Cortinovis, Nicola Ometto, Dawa Artificial Intelligence 68T50 I.2.7 There has been enormous interest in generative AI since ChatGPT was launched in 2022. However, there are concerns about the accuracy and consistency of the outputs of generative AI. We have carried out an exploratory study on the application of this new technology in research data processing. We identified tasks for which rule-based or traditional machine learning approaches were difficult to apply, and then performed these tasks using generative AI. We demonstrate the feasibility of using the generative AI model Claude 3 Opus in three research projects involving complex data processing tasks: 1) Information extraction: We extract plant species names from historical seedlists (catalogues of seeds) published by botanical gardens. 2) Natural language understanding: We extract certain data points (name of drug, name of health indication, relative effectiveness, cost-effectiveness, etc.) from documents published by Health Technology Assessment organisations in the EU. 3) Text classification: We assign industry codes to projects on the crowdfunding website Kickstarter. We share the lessons we learnt from these use cases: How to determine if generative AI is an appropriate tool for a given data processing task, and if so, how to maximise the accuracy and consistency of the results obtained. |
| title | Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases |
| topic | Artificial Intelligence 68T50 I.2.7 |
| url | https://arxiv.org/abs/2504.15829 |