ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey
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Abstract (arXiv)
ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing (NLP) and has pushed the boundaries of LLM capabilities. ChatGPT has played a pivotal role in enabling widespread public interaction with generative artificial intelligence (GAI) on a large scale. It has also sparked research interest in developing similar technologies and investigating their applications and implications. In this paper, our primary goal is to provide a concise survey on the current lines of research on ChatGPT and its evolution. We considered both the glass box and black box views of ChatGPT, encompassing the components and foundational elements of the technology, as well as its applications, impacts, and implications. The glass box approach focuses on understanding the inner workings of the technology, and the black box approach embraces it as a complex system, and thus examines its inputs, outputs, and effects. This paves the way for a comprehensive exploration of the technology and provides a road map for further research and experimentation. We also lay out essential foundational literature on LLMs and GAI in general and their connection with ChatGPT. This overview sheds light on existing and missing research lines in the emerging field of LLMs, benefiting both public users and developers. Furthermore, the paper delves into the broad spectrum of applications and significant concerns in fields such as education, research, healthcare, finance, etc.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 79 of its 226 references that are in the map. First posted on arXiv 9 Jul 2023.
Builds on
79 of 226 references.
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Citing papers per year: 2023: 1; 2024: 5; 2025: 3; 2026: 1.
10 papers.
- 2024Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Carecited by 5
- 2024Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networkscited by 3
- 2023Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Papercited by 2
- 2024Evaluating the Efficacy of Large Language Models in Identifying Phishing Attemptscited by 2
- 2024LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering Systemcited by 1
- 2025EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spacescited by 1
- 2024Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoderscited by 0
- 2025Grasp-HGN: Grasping the Unexpectedcited by 0
- 2025Agentic Meta-Orchestrator for Multi-task Copilotscited by 0
- 2026Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUscited by 0
- 2026Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs29 Jun 2026
- 2025Agentic Meta-Orchestrator for Multi-task Copilots26 Oct 2025
- 2025Grasp-HGN: Grasping the Unexpected11 Aug 2025
- 2025EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces28 Feb 2025
- 2024Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks27 Nov 2024
- 2024Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders12 Nov 2024
- 2024Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care7 Jun 2024
- 2024Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts23 Apr 2024
- 2024LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System5 Feb 2024
- 2023Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper22 Nov 2023