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

TL;DR

This paper evaluates DeepSeek-R1 against o1-preview across seven humanities and social sciences domains, using datasets such as Cherokee-English translations, SciQ, LogiQA, and ACA policy analyses. It combines Transformer MoE architecture, reinforcement learning, and chain-of-thought reasoning to assess strengths in efficient reasoning, detailed explanation, and cost efficiency. Findings show that DeepSeek-R1 often offers deeper analytical explanations and broader synthesis, while o1-preview provides concise, data-driven, and highly accurate responses. The results indicate broad potential for LLMs to enhance text analysis, education, and policy analysis in humanities and social sciences, while highlighting that human expertise remains essential for flexible, context-aware instruction and interpretation.

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.

Figures (32)

• Figure 1: Low-Resource Language Translation: Case1. Both DeepSeek-R1 and o1-preview correctly translate the main information, but the details are slightly different.
• Figure 2: Low-Resource Language Translation: Case2. Both DeepSeek-R1 and o1-preview face challenges in handling the subtle contextual details and precision required for more complex scenes.
• Figure 3: Student Writing Improvement in Higher Education:Case1. Example of modifying and improving the language accuracy of the following text.
• Figure 4: Student Writing Improvement in Higher Education:Case2. Example of appropriate coherence methods to enhance the coherence and flow of the following text
• Figure 5: Student Writing Improvement in Higher Education:Case3. Brief writing example given on the topic