From ChatGPT to DeepSeek AI: A Comprehensive Analysis of Evolution, Deviation, and Future Implications in AI-Language Models
Simrandeep Singh, Shreya Bansal, Abdulmotaleb El Saddik, Mukesh Saini
TL;DR
The paper examines the evolution from OpenAI's ChatGPT to the DeepSeek AI paradigm, focusing on architectural innovations, training strategies, efficiency, and ethical considerations. It contextualizes this shift within the broader history of transformer-based LLMs, highlighting GPT-1 through GPT-4's capabilities and limitations, and introduces DeepSeek AI's GRPO-based RL framework, memory-augmented design, and privacy-conscious, domain-specific refinements. A comprehensive 24-domain case study with 1429 multiple-choice questions demonstrates DeepSeek AI achieving higher overall accuracy (87%) compared to ChatGPT (79%), with notable gains in Mathematics and Tourism and parity in some domains. The work argues that DeepSeek AI's improvements in efficiency, contextual retention, debiasing, and explainability have meaningful practical implications, while outlining future research directions in efficient training, multimodal integration, continuous learning, and robust ethical governance.
Abstract
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.