A Comprehensive Review of DeepSeek: Performance, Architecture and Capabilities

Abstract

This paper provides an extensive review of DeepSeek, an emerging open-source large language model (LLM) known for its Mixture-of-Experts (MoE) architecture and Multi-Head Latent Attention innovations. The study highlights DeepSeek's superior efficiency, scalability, and performance across tasks such as natural language processing, mathematical reasoning, and code generation, positioning it as a competitive alternative to proprietary models like ChatGPT, Claude, and Gemini. Comparative evaluations reveal its strengths in formal writing, structured reasoning, and diagnostic applications in healthcare and finance, while noting challenges in creative tasks and user safety concerns. With a focus on democratizing AI, DeepSeek's cost-efficient, open-source nature fosters accessibility and collaboration across industries such as education, business, and healthcare. Ethical considerations and future directions, including multimodal integrations and enhanced safety protocols, are also explored. Overall, the paper underscores DeepSeek's potential in driving innovation and expanding the frontiers of artificial intelligence research and applications. Comparative analyses reveal that DeepSeek excels in tasks requiring structured writing, grammatical precision, and technical problem-solving. For instance, it achieves notable success in healthcare diagnostics and risk management in finance. However, challenges include its limitations in creative outputs and a higher rate of unsafe responses compared to some competitors, signaling the need for enhanced safety protocols. The paper also highlights user feedback, which is generally positive regarding accessibility and reasoning capabilities, though criticisms are directed at content policies and moderation. DeepSeek's open-source nature is celebrated for democratizing AI, making advanced technology accessible to researchers, educators, and developers worldwide, particularly in resource-constrained settings. Applications across education, healthcare, and finance demonstrate its versatility, from personalizing learning experiences to improving diagnostic accuracy and enabling better financial decision-making. Future directions include expanding its multimodal capabilities, refining safety measures, and exploring innovative applications to maximize its impact across industries.

Ähnliche Arbeiten