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From ChatGPT to DeepSeek AI: A Comprehensive Analysis of Evolution, Deviation, and Future Implications in AI-Language Models

arXiv 2504.03219 ↗First posted 4 Apr 2025cs.CVcited by 0

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Abstract (arXiv)

The rapid advancement of artificial intelligence (AI) has reshaped the field of natural language processing (NLP), with models like OpenAI ChatGPT and DeepSeek AI. Although ChatGPT established a strong foundation for conversational AI, DeepSeek AI introduces significant improvements in architecture, performance, and ethical considerations. This paper presents a detailed analysis of the evolution from ChatGPT to DeepSeek AI, highlighting their technical differences, practical applications, and broader implications for AI development. To assess their capabilities, we conducted a case study using a predefined set of multiple choice questions in various domains, evaluating the strengths and limitations of each model. By examining these aspects, we provide valuable insight into the future trajectory of AI, its potential to transform industries, and key research directions for improving AI-driven language models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 67 references that are in the map. First posted on arXiv 4 Apr 2025.

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References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  7. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  8. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  9. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  10. 2016Deep Learning with Differential Privacycited by 2,682
  11. 2020Knowledge Distillation: A Surveycited by 1,578
  12. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
11 more
  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  2. 2021Transformers in Vision: A Surveycited by 1,009
  3. 2021GPT Understands, Toocited by 715
  4. 2021Transformer in Transformercited by 632
  5. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 415
  6. 2019An Attentive Survey of Attention Modelscited by 123
  7. 2021Towards artificial general intelligence via a multimodal foundation modelcited by 112
  8. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  9. 2023ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishingcited by 51
  10. 2020Insights into Performance Fitness and Error Metrics for Machine Learningcited by 12
  11. 2021The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systemscited by 8

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