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A brief Review of ChatGPT: Limitations, Challenges and Ethical-Social Implications

Authors: Naem Azam;

A brief Review of ChatGPT: Limitations, Challenges and Ethical-Social Implications

Abstract

This research aims to provide a comprehensive overview of the limitations, challenges, and ethical-social implications associated with the use of ChatGPT, a powerful conversational AI model developed by OpenAI. The study begins by describing the development of GPT and its evolution over the years, and its impact on the NLP community. The limitations of ChatGPT, including its inability to handle complex conversational scenarios, its dependence on large amounts of training data, and its potential to perpetuate biases and stereotypes, are then discussed in detail. The challenges associated with ChatGPT, such as ensuring its accuracy and reliability, integrating multi-modal inputs, personalizing and customizing its behavior, and making its interactions more human-like and natural, are also explored. The study then highlights the important ethical and social implications associated with the widespread use of ChatGPT, including issues related to privacy, security, and responsible use. The research concludes by emphasizing the importance of continued research and development in the field of NLP, and the need to carefully consider the limitations, challenges, and ethical-social implications associated with the use of ChatGPT in order to fully realize its potential as a powerful tool for advancing NLP research and improving human-computer interactions.

{"references": ["Brown, Tom B., et al. \"Language Models Are Few-Shot Learners.\" Arxiv.org, 28 May 2020, arxiv.org/abs/2005.14165.", "Conneau, Alexis, et al. \"Unsupervised Cross-Lingual Representation Learning for Speech Recognition.\" ArXiv:2006.13979 [Cs, Eess], 15 Dec. 2020, arxiv.org/abs/2006.13979.", "Devlin, Jacob, et al. GPT-3: Language Models and the Future of NLP. 2020. This paper provides an overview of GPT-3, the latest and largest GPT model developed by OpenAI, and its potential impact on NLP and AI more broadly.", "Eliot, Lance. \"Generative Pre-Trained Transformers (GPT-3) Pertain to AI in the Law.\" SSRN Electronic Journal, 2021, https://doi.org/10.2139/ssrn.3974887. Accessed 13 May 2022.", "Openai, Alec, et al. Improving Language Understanding by Generative Pre-Training. 2018", "Peng, H, and Open ai. Fine-Tuning Pretrained Transformers for Natural Language Inference. 2020. This paper explores the use of pretrained language models like GPT for NLP tasks such as natural language inference.", "Radford, Alec, et al. Language Models Are Unsupervised Multitask Learners", "Wernick, J, and B. Chiang. Generative Pre-Trained Transformer 3 (GPT-3): A Deep Dive. 2020. This paper provides a comprehensive overview of GPT-3, including its architecture, training data, and capabilities."]}

Keywords

ChatGPT, Microsoft, OpenAI, Limitations, Ethical-Social Implications, Challenges, NLP

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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