A Comparative Study: Evaluating ChatGPT and DeepSeek AI Tools in Practice
Abstract
The growing integration of artificial intelligence (AI) across industries has led to the development of numerous AI-powered tools, each designed to address specific user needs. Among these tools, ChatGPT, developed by OpenAI, and DeepSeek AI, a specialized model aimed at technical applications, have garnered significant attention. This study provides a comprehensive comparative evaluation of ChatGPT and DeepSeek AI, focusing on multiple criteria such as accuracy, usability, response coherence, domain-specific knowledge, and computational efficiency. Through practical implementations in real-world scenarios, the research highlights the performance differences between the two models. ChatGPT excels in general-purpose tasks, demonstrating its versatility in conversational capabilities, creativity, and content generation. In contrast, DeepSeek AI shines in specialized fields, providing precise, domain-specific responses, particularly in areas such as technical problem-solving and scientific research. The analysis explores the strengths and weaknesses of both tools, offering valuable insights into their practical applications across various industries. This research aims to guide users, whether researchers, businesses, or content creators, in choosing the most suitable AI tool for their needs. The findings also pave the way for future advancements in AI development, highlighting opportunities to enhance both general-purpose and specialized models for broader applicability.
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Liang, P., et al. (2022). "Holistic Evaluation of Language Models (HELM)." Stanford University.
Rajpurkar, P., et al. (2016). "SQuAD: 100,000+ Questions for Machine Comprehension of Text." Proceedings of EMNLP.
Wang, A., et al. (2018). "GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding." Proceedings of ICLR.
Bender, E. M., et al. (2021). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" FAccT Conference.
Brown, T., et al. (2020). "Language Models Are Few-Shot Learners." NeurIPS.
Zhao, R., & Li, H. (2022). "Performance Benchmarks of AI Language Models in Business and Research Applications." Journal of AI Research.
Smith, J., et al. (2023). "Assessing the Capabilities of DeepSeek AI in Specialized Domains." AI & Society.
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