Bridging Technology and Humanities: Evaluating the Impact of Large Language Models on Social Sciences Research with DeepSeek-R1

2025-03-20

AI Summary

This study systematically evaluates the applicability and pedagogical suitability of large language models (LLMs) in empirical research within the humanities and social sciences. Focusing on DeepSeek-R1, it conducts multidisciplinary empirical experiments across seven domains: low-resource language translation, educational Q&A, academic writing assistance, logical reasoning, psychometric analysis, public health policy evaluation, and arts education. It introduces “self-generated reasoning process” as a novel, interpretable metric for assessing novice-friendly AI research assistants. Using a comparative experimental framework against o1-preview, the evaluation integrates domain-expert annotation, answer plausibility scoring, and explanation completeness assessment. Results demonstrate that DeepSeek-R1 achieves higher accuracy, produces clearer reasoning chains, and delivers more comprehensive explanations—particularly excelling in instructional support tasks. These findings validate its practical utility in enhancing research efficiency and broadening knowledge accessibility in the social sciences.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In recent years, the development of Large Language Models (LLMs) has made significant breakthroughs in the field of natural language processing and has gradually been applied to the field of humanities and social sciences research. LLMs have a wide range of application value in the field of humanities and social sciences because of its strong text understanding, generation and reasoning capabilities. In humanities and social sciences research, LLMs can analyze large-scale text data and make inferences. This article analyzes the large language model DeepSeek-R1 from seven aspects: low-resource language translation, educational question-answering, student writing improvement in higher education, logical reasoning, educational measurement and psychometrics, public health policy analysis, and art education.Then we compare the answers given by DeepSeek-R1 in the seven aspects with the answers given by o1-preview. DeepSeek-R1 performs well in the humanities and social sciences, answering most questions correctly and logically, and can give reasonable analysis processes and explanations. Compared with o1-preview, it can automatically generate reasoning processes and provide more detailed explanations, which is suitable for beginners or people who need to have a detailed understanding of this knowledge, while o1-preview is more suitable for quick reading. Through analysis, it is found that LLM has broad application potential in the field of humanities and social sciences, and shows great advantages in improving text analysis efficiency, language communication and other fields. LLM's powerful language understanding and generation capabilities enable it to deeply explore complex problems in the field of humanities and social sciences, and provide innovative tools for academic research and practical applications.

Problem

• Evaluating DeepSeek-R1's impact on humanities and social sciences research.
• Comparing DeepSeek-R1 with o1-preview in seven application areas.
• Exploring LLMs' potential in improving text analysis and language communication.

Innovation

• DeepSeek-R1 excels in low-resource language translation.
• DeepSeek-R1 improves student writing in higher education.
• DeepSeek-R1 provides detailed logical reasoning explanations.