# ChatGPT: Applications, Opportunities, and Threats

arXiv: https://arxiv.org/abs/2304.09103 · HTML: https://mlsanity.com/paper/2304.09103/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)

## Built on by (most cited first, 28 of 28)

- [ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming](https://mlsanity.com/paper/2404.08676/) (2024)
- [A Complete Survey on LLM-based AI Chatbots](https://mlsanity.com/paper/2406.16937/) (2024)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games](https://mlsanity.com/paper/2305.14879/) (2023)
- [PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation](https://mlsanity.com/paper/2501.11551/) (2025)
- [Surprising gender biases in GPT](https://mlsanity.com/paper/2407.06003/) (2024)
- [Urban Safety Perception Assessments via Integrating Multimodal Large Language Models with Street View Images](https://mlsanity.com/paper/2407.19719/) (2024)
- [Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things](https://mlsanity.com/paper/2501.00906/) (2025)
- [AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application](https://mlsanity.com/paper/2503.04788/) (2025)
- [EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval](https://mlsanity.com/paper/2510.18546/) (2025)
- [How User Language Affects Conflict Fatality Estimates in ChatGPT](https://mlsanity.com/paper/2308.00072/) (2023)
- [AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models](https://mlsanity.com/paper/2308.04241/) (2023)
- [Behind the Screen: Investigating ChatGPT's Dark Personality Traits and Conspiracy Beliefs](https://mlsanity.com/paper/2402.04110/) (2024)
- [Is GPT-4 Less Politically Biased than GPT-3.5? A Renewed Investigation of ChatGPT's Political Biases](https://mlsanity.com/paper/2410.21008/) (2024)
- [Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment](https://mlsanity.com/paper/2508.08629/) (2025)
- [HouYi: An open-source large language model specially designed for renewable energy and carbon neutrality field](https://mlsanity.com/paper/2308.01414/) (2023)
- [Experiences with Content Development and Assessment Design in the Era of GenAI](https://mlsanity.com/paper/2503.00081/) (2025)
- [Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing](https://mlsanity.com/paper/2506.03501/) (2025)
- [Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance](https://mlsanity.com/paper/2506.18501/) (2025)
- [Learning from Preferences and Mixed Demonstrations in General Settings](https://mlsanity.com/paper/2508.14027/) (2025)
- [Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency](https://mlsanity.com/paper/2608.14065/) (2026)
- [Critical Role of Artificially Intelligent Conversational Chatbot](https://mlsanity.com/paper/2310.20474/) (2023)
- [Evaluation Methodology for Large Language Models for Multilingual Document Question and Answer](https://mlsanity.com/paper/2402.01065/) (2024)
- [From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process](https://mlsanity.com/paper/2402.01717/) (2024)
- [Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners](https://mlsanity.com/paper/2407.15508/) (2024)
- [Research on Predicting Public Opinion Event Heat Levels Based on Large Language Models](https://mlsanity.com/paper/2409.18548/) (2024)
- [Virtual Fashion Photo-Shoots: Building a Large-Scale Garment-Lookbook Dataset](https://mlsanity.com/paper/2510.00633/) (2025)
- [Poisson Hyperplane Processes with Rectified Linear Units](https://mlsanity.com/paper/2601.05586/) (2026)

## Abstract (arXiv)

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.