Exploring ChatGPT and its Impact on Society
Not on the map. Why?
Abstract (arXiv)
Artificial intelligence has been around for a while, but suddenly it has received more attention than ever before. Thanks to innovations from companies like Google, Microsoft, Meta, and other major brands in technology. OpenAI, though, has triggered the button with its ground-breaking invention ChatGPT. ChatGPT is a Large Language Model (LLM) based on Transformer architecture that has the ability to generate human-like responses in a conversational context. It uses deep learning algorithms to generate natural language responses to input text. Its large number of parameters, contextual generation, and open-domain training make it a versatile and effective tool for a wide range of applications, from chatbots to customer service to language translation. It has the potential to revolutionize various industries and transform the way we interact with technology. However, the use of ChatGPT has also raised several concerns, including ethical, social, and employment challenges, which must be carefully considered to ensure the responsible use of this technology. The article provides an overview of ChatGPT, delving into its architecture and training process. It highlights the potential impacts of ChatGPT on the society. In this paper, we suggest some approaches involving technology, regulation, education, and ethics in an effort to maximize ChatGPT's benefits while minimizing its negative impacts. This study is expected to contribute to a greater understanding of ChatGPT and aid in predicting the potential changes it may bring about.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 20 of its 90 references that are in the map. First posted on arXiv 21 Feb 2024.
Builds on
20 of 90 references.
References · 20
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8 more
- 2023ChatGPT: Jack of all trades, master of nonecited by 204
- 2023A Categorical Archive of ChatGPT Failurescited by 167
- 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
- 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 137
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Built on by
Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2024The Sociolinguistic Foundations of Language Modelingcited by 13
- 2024LLMs: A Game-Changer for Software Engineers?cited by 11
- 2025Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilotcited by 2
- 2025AttentionSmithy: A Modular Framework for Rapid Transformer Development and Customizationcited by 0
- 2025Assessing and Refining ChatGPT's Performance in Identifying Targeting and Inappropriate Language: A Comparative Studycited by 0
- 2026Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrievalcited by 0
- 2026Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval18 Mar 2026
- 2025Assessing and Refining ChatGPT's Performance in Identifying Targeting and Inappropriate Language: A Comparative Study27 May 2025
- 2025AttentionSmithy: A Modular Framework for Rapid Transformer Development and Customization13 Feb 2025
- 2025Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot12 Jan 2025
- 2024LLMs: A Game-Changer for Software Engineers?1 Nov 2024
- 2024The Sociolinguistic Foundations of Language Modeling12 Jul 2024