Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916642354626560 |
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| author | Maiti, Aniruddha Adewumi, Samuel Tikure, Temesgen Alemayehu Wang, Zichun Sengupta, Niladri Sukhanova, Anastasiia Jana, Ananya |
| author_facet | Maiti, Aniruddha Adewumi, Samuel Tikure, Temesgen Alemayehu Wang, Zichun Sengupta, Niladri Sukhanova, Anastasiia Jana, Ananya |
| contents | This study examines how large language models categorize sentences from scientific papers using prompt engineering. We use two advanced web-based models, GPT-4o (by OpenAI) and DeepSeek R1, to classify sentences into predefined relationship categories. DeepSeek R1 has been tested on benchmark datasets in its technical report. However, its performance in scientific text categorization remains unexplored. To address this gap, we introduce a new evaluation method designed specifically for this task. We also compile a dataset of cleaned scientific papers from diverse domains. This dataset provides a platform for comparing the two models. Using this dataset, we analyze their effectiveness and consistency in categorization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering Maiti, Aniruddha Adewumi, Samuel Tikure, Temesgen Alemayehu Wang, Zichun Sengupta, Niladri Sukhanova, Anastasiia Jana, Ananya Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition This study examines how large language models categorize sentences from scientific papers using prompt engineering. We use two advanced web-based models, GPT-4o (by OpenAI) and DeepSeek R1, to classify sentences into predefined relationship categories. DeepSeek R1 has been tested on benchmark datasets in its technical report. However, its performance in scientific text categorization remains unexplored. To address this gap, we introduce a new evaluation method designed specifically for this task. We also compile a dataset of cleaned scientific papers from diverse domains. This dataset provides a platform for comparing the two models. Using this dataset, we analyze their effectiveness and consistency in categorization. |
| title | Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.02032 |