mlsanityData to 7 Oct 2026

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

arXiv 2503.16304 ↗First posted 20 Mar 2025cs.CY cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 21 of its 44 references that are in the map. First posted on arXiv 20 Mar 2025.

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21 of 44 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Brief analysis of DeepSeek R1 and its implications for Generative AI4 Feb
  2. 2024DeepSeek-V3 Technical Report27 Dec

Other references · 19

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  4. 2017Crowdsourcing Multiple Choice Science Questionscited by 742
  5. 2017Deep Reinforcement Learning: An Overviewcited by 327
  6. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
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  10. 2025Reinforcement Learning with Verifiable Rewards: GRPO's Effective Loss, Dynamics, and Success Amplificationcited by 108
  11. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  12. 2024Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?cited by 26
7 more
  1. 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 26
  2. 2024Progressive Mixed-Precision Decoding for Efficient LLM Inferencecited by 17
  3. 2024MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Designcited by 17
  4. 2024Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Languagecited by 12
  5. 2025Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessmentcited by 7
  6. 2025Unraveling the Localized Latents: Learning Stratified Manifold Structures in LLM Embedding Space with Sparse Mixture-of-Expertscited by 7
  7. 2023Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessmentscited by 4

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  2. 2024Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research
  3. 2025DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
  4. 2025HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
  5. 2025Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
  6. 2025A Review of DeepSeek Models' Key Innovative Techniques
  7. 2025Deep Research: A Systematic Survey
  8. 2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermism