Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering

This study examines how large language models categorize sentences from scientific papers using prompt engineering.We use two advanced web-based models, OpenAI's GPT-4o 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.

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Publication Details

Published
2025-04-29
DOI
https://doi.org/10.18260/1-2--54654
Citations
2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
4.96
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article

Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering

Aniruddha Maiti, Sunday Eric Adewumi, Ananya Jana, Zichun Wang et al.
2 citations
Topic Modeling
4.96
article

Comparative Analysis of OpenAI GPT-4o and DeepSeek R1 for Scientific Text Categorization Using Prompt Engineering

Aniruddha Maiti, Sunday Eric Adewumi, Ananya Jana, Zichun Wang, Anastasiia Sukhanova, Nandita Sengupta, Temesgen Alemayehu Tikure
article en
2 citations

Abstract

This study examines how large language models categorize sentences from scientific papers using prompt engineering.We use two advanced web-based models, OpenAI's GPT-4o 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.

Marshall University (US)
Openalex Percentile: Top 6%
Topic Modeling
4.96
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