大型语言模型在教育与研究中的应用：基于实证和用户调查的分析
Large Language Models for Education and Research: An Empirical and User Survey-based Analysis
AI总结：
本文通过实证和用户调查分析，评估ChatGPT和DeepSeek在教育与研究中的表现，揭示其在语言理解、编程和专业问题解决中的优势与局限。
摘要：
预训练的大规模语言模型（LLMs）在多个领域取得了显著成功，教育和研究尤为突出。在当前最先进的LLMs中，ChatGPT和DeepSeek在数学、科学、医学、文学和编程方面表现出色。在本研究中，我们通过背景技术分析、实证实验和真实用户调查对这两种LLMs进行了全面评估。评估探讨了模型准确性、计算效率和用户体验之间的权衡问题，在教育和研究领域中。我们通过文本生成、编程和专业问题解决任务对这些LLMs进行了基准测试。实验结果表明，ChatGPT在一般语言理解和文本生成方面表现优异，而DeepSeek由于其以效率为导向的设计，在编程任务中表现出色。此外，这两种模型在医学诊断输出方面具有准确性和在解决复杂数学问题方面表现良好。这些定量发现补充了对学生、教育工作者和研究人员的调查，突显了这些模型在推动教育和研究发展中的实际效益和局限性。
英文摘要：
Pretrained Large Language Models (LLMs) have achieved remarkable success across diverse domains, with education and research emerging as particularly impactful areas. Among current state-of-the-art LLMs, ChatGPT and DeepSeek exhibit strong capabilities in mathematics, science, medicine, literature, and programming. In this study, we present a comprehensive evaluation of these two LLMs through background technology analysis, empirical experiments, and a real-world user survey. The evaluation explores trade-offs among model accuracy, computational efficiency, and user experience in educational and research affairs. We benchmarked these LLMs performance in text generation, programming, and specialized problem-solving. Experimental results show that ChatGPT excels in general language understanding and text generation, while DeepSeek demonstrates superior performance in programming tasks due to its efficiency-focused design. Moreover, both models deliver medically accurate diagnostic outputs and effectively solve complex mathematical problems. Complementing these quantitative findings, a survey of students, educators, and researchers highlights the practical benefits and limitations of these models, offering deeper insights into their role in advancing education and research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
