Generative AI
Not on the map. Why?
Abstract (arXiv)
The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research.
Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). Builds on 48 of its 134 references that are in the map. First posted on arXiv 13 Sep 2023.
Builds on
48 of 134 references.
References · 48
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2023GPT-4 Technical Reportcited by 18,132
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2016Improved Techniques for Training GANscited by 2,938
36 more
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
- 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
- 2022Competition-Level Code Generation with AlphaCodecited by 1,427
- 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
- 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
- 2019Green AIcited by 620
- 2023MusicLM: Generating Music From Textcited by 442
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
- 2023How is ChatGPT's behavior changing over time?cited by 265
- 2020The Creation and Detection of Deepfakes: A Surveycited by 246
- 2018Machine Theory of Mindcited by 222
- 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
- 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
- 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
- 2023The Capacity for Moral Self-Correction in Large Language Modelscited by 171
- 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
- 2022Human heuristics for AI-generated language are flawedcited by 104
- 2023AI model GPT-3 (dis)informs us better than humanscited by 75
- 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
- 2022TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversationscited by 65
- 2020The Unreasonable Effectiveness of Deep Learning in Artificial Intelligencecited by 57
- 2021Hybrid Intelligencecited by 55
- 2022Evidence for Hypodescent in Visual Semantic AIcited by 30
- 2023Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Noveltycited by 27
- 2021Machine learning and deep learningcited by 26
- 2018Deep learning in business analytics and operations research: Models, applications and managerial implicationscited by 24
- 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
- 2023Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativitycited by 24
- 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
- 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
- 2023Just Tell Me: Prompt Engineering in Business Process Managementcited by 17
- 2019Interactive AI with a Theory of Mindcited by 7
- 2022GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintscited by 6
- 2022A Survey of Text Representation Methods and Their Genealogycited by 5
- 2023HQP: A Human-Annotated Dataset for Detecting Online Propagandacited by 4
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Citing papers per year: 2023: 6; 2024: 32; 2025: 57; 2026: 24.
119 papers.
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- 2026Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach2 May 2026
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- 2026Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models25 Apr 2026
- 2026Culturally uneven urban perception in large language models21 Apr 2026
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- 2026Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities19 Mar 2026
- 2025Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting1 Nov 2025
- 2025When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents13 Oct 2025
- 2025ThreatGPT: An Agentic AI Framework for Enhancing Public Safety through Threat Modeling4 Sep 2025
- 2025Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework7 Jul 2025
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- 2025Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models13 Feb 2025
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- 2023Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models15 Nov 2023
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- 2026Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models25 Apr 2026
- 2026Culturally uneven urban perception in large language models21 Apr 2026
- 2026Culturally Adaptive Explainable LLM Assessment for Multilingual Information Disorder: A Human-in-the-Loop Approach28 Mar 2026
- 2026Is the future of AI green? What can innovation diffusion models say about generative AI's environmental impact?22 Mar 2026
- 2026Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms20 Mar 2026
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