# ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey arXiv: https://arxiv.org/abs/2307.04251 ยท HTML: https://mlsanity.com/paper/2307.04251/ ## 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 (most cited first, 30 of 79) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021) - [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020) - [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023) - [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022) - [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021) - [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022) - [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016) - [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023) - [Vector-quantized Image Modeling with Improved VQGAN](https://mlsanity.com/paper/2110.04627/) (2021) - [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023) - [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023) - [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021) - [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022) ## Built on by (most cited first, 10 of 10) - [Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care](https://mlsanity.com/paper/2406.06616/) (2024) - [Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks](https://mlsanity.com/paper/2412.00090/) (2024) - [Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper](https://mlsanity.com/paper/2311.13126/) (2023) - [Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts](https://mlsanity.com/paper/2404.15485/) (2024) - [LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System](https://mlsanity.com/paper/2402.05130/) (2024) - [EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces](https://mlsanity.com/paper/2503.00092/) (2025) - [Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders](https://mlsanity.com/paper/2411.07870/) (2024) - [Grasp-HGN: Grasping the Unexpected](https://mlsanity.com/paper/2508.07648/) (2025) - [Agentic Meta-Orchestrator for Multi-task Copilots](https://mlsanity.com/paper/2510.22781/) (2025) - [Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs](https://mlsanity.com/paper/2606.30391/) (2026) ## 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.