Relevant bibliographies by topics / DeepSeek-R1

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Academic literature on the topic 'DeepSeek-R1'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "DeepSeek-R1"

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Hayder, Wrya Anwar. "Highlighting DeepSeek-R1: Architecture, Features and Future Implications." International Journal of Computer Science and Mobile Computing 14, no. 2 (2025): 1–13. https://doi.org/10.47760/ijcsmc.2025.v14i02.001.

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Large language models have taken the central stage in artificial intelligence, but they confront challenges like high computational costs, strong limitations on scaling, and difficulty adapting to new tasks. In contrast, DeepSeek-R1, used extensively, addresses such issues using architecture insights, novel learning paradigms, and optimization approaches. In this paper, a high-level comparison is made of DeepSeek-R1 versus generic LLMs. In this article generic LLMs refer to popular models predating DeepSeek-R1, such as OpenAI's GPT-4, Meta's Llama, and Google's PaLM, which rely on other archit
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李昌奎. "ChatGPT与DeepSeek-R1比较研究：架构、推理能力与应用场景分析A Comparative Study of ChatGPT and DeepSeek-R1: Analysis of Architecture, Reasoning Capabilities, and Application Scenarios". Theory and Practice of Social Science 7, № 2 (2025): 18–31. https://doi.org/10.6914/tpss.070202.

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人工智能技术的飞速发展推动了大语言模型（LLM）的不断进步。在众多LLM中，OpenAI推出的ChatGPT和DeepSeek-AI开发的DeepSeek-R1尤为引人注目。ChatGPT基于GPT-4架构，具备强大的自然语言理解能力和广泛的应用场景，而DeepSeek-R1则通过强化学习方法优化推理能力，在数学推理和编程任务中展现了强劲的竞争力。本文基于DeepSeek-R1的最新研究成果，全面对比ChatGPT与DeepSeek-R1在模型架构、训练方法、推理能力、应用场景及开放性等方面的差异。研究发现，ChatGPT依赖监督微调（SFT）和基于人类反馈的强化学习（RLHF），在自然语言处理任务上表现突出，而DeepSeek-R1更倾向于通过强化学习优化推理能力，尤其在数学推理、代码生成等任务上表现优异。此外，ChatGPT采用闭源策略，主要用于商业应用，而DeepSeek-R1则采取开源模式，为研究社区和开发者提供更大的灵活性。本文的研究结果为人工智能研究人员和开发者提供了重要参考，以期促进LLM技术的发展，并为未来的大模型优化提供新思路。 The rapid development of artificial intelligence has driven the continuous advancement of large language models (LLMs).
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Chan, Lining, Xinjie Xu, and Kaiyang Lv. "DeepSeek-R1 and GPT-4 are comparable in a complex diagnostic challenge: a historical control study." International Journal of Surgery 111, no. 6 (2025): 4056–59. https://doi.org/10.1097/js9.0000000000002386.

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Background: Large language models (LLMs) have demonstrated potential in medical diagnostics, but their accuracy in complex cases remains a subject of investigation. DeepSeek-R1, an open-source model with advanced reasoning capabilities, has gained global attention. This study evaluates the diagnostic performance of DeepSeek-R1 compared to GPT-4 in complex clinical cases. Materials and methods: A historical control study was conducted using 100 clinicopathologic cases from the New England Journal of Medicine (NEJM), published between 18 August 2022, and 30 January 2025. Each case was processed
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ZHANG, Huimin. "How DeepSeek-R1 was created?" Journal of Shenzhen University Science and Engineering 42, no. 2 (2025): 226–32. https://doi.org/10.3724/sp.j.1249.2025.02226.

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Qin, Wenting, Lijie Suo, Liangchen Li, and Fan Yang. "Advancing Software Vulnerability Detection with Reasoning LLMs: DeepSeek-R1′s Performance and Insights." Applied Sciences 15, no. 12 (2025): 6651. https://doi.org/10.3390/app15126651.

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The increasing complexity of software systems has heightened the need for efficient and accurate vulnerability detection. Large Language Models have emerged as promising tools in this domain; however, their reasoning capabilities and limitations remain insufficiently explored. This study presents a systematic evaluation of different Large Language Models with and without explicit reasoning mechanisms, including Claude-3.5-Haiku, GPT-4o-Mini, DeepSeek-V3, O3-Mini, and DeepSeek-R1. Experimental results demonstrate that reasoning-enabled models, particularly DeepSeek-R1, outperform their non-reas
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Meo, Sultan Ayoub, Farah A. Abukhalaf, Riham A. ElToukhy, and Kamran Sattar. "Exploring the role of DeepSeek-R1, ChatGPT-4, and Google Gemini in medical education: How valid and reliable are they?" Pakistan Journal of Medical Sciences 41, no. 7 (2025): 1887–92. https://doi.org/10.12669/pjms.41.7.12183.

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Objective: In recent years, Artificial Intelligence (AI) has led to rapid advancements in science, technology, industries, healthcare settings, and medical education. A Chinese-built large language model, DeepSeek-R1, inspires the scientific community as an affordable and open alternative to earlier established US-based AI models, ChatGPT-4 and Google Gemini 1.5 Pro. This study aimed to explore the role of “DeepSeek-R1, ChatGPT-4 and Google Gemini 1.5 Pro” and to assess the validity and reliability of these AI tools in medical education. Methods: The current cross-sectional study was performed
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Sallam, Malik, Israa M. Alasfoor, Shahad W. Khalid, et al. "Chinese generative AI models (DeepSeek and Qwen) rival ChatGPT-4 in ophthalmology queries with excellent performance in Arabic and English." Narra J 5, no. 1 (2025): e2371. https://doi.org/10.52225/narra.v5i1.2371.

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The rapid evolution of generative artificial intelligence (genAI) has ushered in a new era of digital medical consultations, with patients turning to AI-driven tools for guidance. The emergence of Chinese-developed genAI models such as DeepSeek-R1 and Qwen-2.5 presented a challenge to the dominance of OpenAI’s ChatGPT. The aim of this study was to benchmark the performance of Chinese genAI models against ChatGPT-4o and to assess disparities in performance across English and Arabic. Following the METRICS checklist for genAI evaluation, Qwen-2.5, DeepSeek-R1, and ChatGPT-4o were assessed for com
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Jiao, Cheng, Erik Rosas, Hassan Asadigandomani, et al. "Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists." Diagnostics 15, no. 10 (2025): 1221. https://doi.org/10.3390/diagnostics15101221.

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Background/Objectives: This study evaluated the diagnostic accuracy of seven publicly available large language models (LLMs)—GPT-3.5, GPT-4.o Mini, GPT-4.o, Gemini 1.5 Flash, Claude 3.5 Sonnet, Grok3, and DeepSeek R1—in diagnosing corneal diseases, comparing their performance to human specialists. Methods: Twenty corneal disease cases from the University of Iowa’s EyeRounds were presented to each LLM. Diagnostic accuracy was determined by comparing LLM-generated diagnoses to the confirmed case diagnoses. Four human cornea specialists evaluated the same cases to establish a benchmark and assess
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Papaioannou, Ioannis, Christos Korkas, and Elias Kosmatopoulos. "Smart Building Recommendations with LLMs: A Semantic Comparison Approach." Buildings 15, no. 13 (2025): 2303. https://doi.org/10.3390/buildings15132303.

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The increasing need for sustainable energy management in smart buildings calls for cost-effective solutions that balance energy efficiency and occupant comfort. This article presents a Large Language Model (LLM)-based recommendation system capable of generating proactive, context-aware suggestions from dynamic building conditions. The system was trained on a combination of real-world data and Sinergym simulations, capturing inputs such as weather conditions, forecasts, energy usage, electricity prices, and detailed zone parameters. Five models were fine-tuned and evaluated: GPT-2-Small, GPT-2-
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Han, Zongshuo. "Silicon Disruption: An Event Study of DeepSeek R1’s Breakthrough Impact on Semiconductor Markets." SHS Web of Conferences 218 (2025): 01030. https://doi.org/10.1051/shsconf/202521801030.

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This paper conducts an event study to examine the US stock market response to the launch of the DeepSeek R1 model by its Chinese competitor, as well as to assess how US semiconductor manufacturers reacted to this launch. Which is a little new AI-large language model, designed to challenge the performance level of the existing AIs, such as Hudy, Claude-3 and o1-mini. 5. The results from the event study show a significant negative reaction from investors to US semiconductor stocks in response to the release of the DeepSeek R1 model. Furthermore, the effect is stronger among specialized AI servic
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Book chapters on the topic "DeepSeek-R1"

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Du, Junlei, Qinhua Zheng, and Shuang Li. "Leveraging Large Reasoning Models for Test Equating Without Anchor Items: A Simulation Study with O1 and DeepSeek-R1." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-99264-3_17.

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Abraham, Manu Mariyan, and Shampa Dev. "Navigating the Intersection of AI Models, State Surveillance, National Security, and AI Regulation in the Indian Technological Landscape." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1210-1.ch010.

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AI models like DeepSeek are revolutionising the industry with their novel development methods and low production and operation costs. Generative AI offers extraordinary capabilities in its application in different sectors. Despite this, the AI models pose serious threats to national security if allowed to operate without any checks. This paper analyses the components of AI, the type of foundational models, and the challenges it poses in regulating AI. The paper also dissects the effect of AI models on national security, surveillance apparatus and law enforcement. The apparent risk of AI models
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K V, Deepak, Ayush Kottary, and Sanath Kumar K. "AI Revolution in Real Time: Unpacking the Market Ripples of DeepSeek-R1 on Wall Street and Shanghai : An Event Study." In Business Analytics and Intelligence: Driving Strategy with Data. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-7-6-3.

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This study examines the impact of DeepSeek AI, a breakthrough in Large Language Models (LLMs), on the stock market performance of Shanghai Stock Exchange and the Nasdaq Composite Index. Using the event study methodology, the research analyses abnormal returns surrounding the announcement of DeepSeek AI's release. The focus is on technology stocks, with a particular emphasis on AI and disruptive technologies. By gaging Cumulative Average Abnormal Returns (CAAR) and Average Abnormal Returns (AAR), the study assesses the market’s reaction to this technological innovation. The results shed light o
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Conference papers on the topic "DeepSeek-R1"

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Vučićević, Nemanja, Marina Svičević, and Aleksandar Milenković. "Challenging Deepseek-R1 with Serbian High School Math Competition Problems." In Sinteza 2025. Singidunum University, 2025. https://doi.org/10.15308/sinteza-2025-274-280.

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Raza, Muhammad Raheel, Shahbaz Ahmed, Fahad Ahmed Khokhar, and Asaf Varol. "Exploring the Potential of DeepSeek-R1 Model in Transforming Healthcare Solutions: An Overview." In 2025 13th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2025. https://doi.org/10.1109/isdfs65363.2025.11012057.

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Huang, Hoying. "MedeepRAG: A Retrieval-Augmented Generation System for Medical Q&A Using DeepSeek-R1." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11083988.

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Zhang, Qinghe, Zhuopei Cheng, and Zhuqi Wang. "Research and Application of Anti-Money Laundering Transaction Detection based on DeepSeek-R1 Small Model using Knowledge Distillation." In CSAIDE 2025: 2025 4th International Conference on Cyber Security, Artificial Intelligence and the Digital Economy. ACM, 2025. https://doi.org/10.1145/3729706.3729730.

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