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Unveiling the Potential of ChatGPT: Applications, Challenges, and Future Directions

by Hiba Mohsin, Sehba Masood
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 21
Year of Publication: 2023
Authors: Hiba Mohsin, Sehba Masood
10.5120/ijca2023922945

Hiba Mohsin, Sehba Masood . Unveiling the Potential of ChatGPT: Applications, Challenges, and Future Directions. International Journal of Computer Applications. 185, 21 ( Jul 2023), 37-49. DOI=10.5120/ijca2023922945

@article{ 10.5120/ijca2023922945,
author = { Hiba Mohsin, Sehba Masood },
title = { Unveiling the Potential of ChatGPT: Applications, Challenges, and Future Directions },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 21 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 37-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number21/32819-2023922945/ },
doi = { 10.5120/ijca2023922945 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:41.956096+05:30
%A Hiba Mohsin
%A Sehba Masood
%T Unveiling the Potential of ChatGPT: Applications, Challenges, and Future Directions
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 21
%P 37-49
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

ChatGPT is a large language processing model created by OpenAI. It has brought significant improvements to the field of natural language processing, particularly generating text and conversational dialogues as well as answering questions. In this paper, a systematic literature review on ChatGPT has been done. Intelligent approaches and techniques associated with Large Language Models have been discussed. Further, the salient features of ChatGPT including generating human-like responses and understanding natural language have been examined. Moreover, the issues and challenges of ChatGPT such as bias, misinterpretation, security concerns, etc., have been highlighted. Finally, the various applications of ChatGPT across various sectors like healthcare, banking, business, and content generation have been discussed. Overall, this work provides valuable insights for researchers and industries seeking to enhance the performance and application of ChatGPT.

References
  1. Ergen, M., What is artificial intelligence? Technical considerations and future perception. Anatolian J. Cardiol, 2019. 22(2): p. 5-7.
  2. Burns, E., N. Laskowski, and L. Tucci, What is artificial intelligence. Search Enterprise AI, 2021.
  3. Radford, A., et al., Improving language understanding by generative pre-training. 2018.
  4. Devlin, J., et al., Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  5. Liu, Y., et al., Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
  6. Clark, E., et al., All that's' human'is not gold: Evaluating human evaluation of generated text. arXiv preprint arXiv:2107.00061, 2021.
  7. Baidoo-Anu, D. and L. Owusu Ansah, Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484, 2023.
  8. Susnjak, T., ChatGPT: The End of Online Exam Integrity? arXiv preprint arXiv:2212.09292, 2022.
  9. Kasneci, E., et al., ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 2023. 103: p. 102274.
  10. Khalil, M. and E. Er, Will ChatGPT get you caught? Rethinking of plagiarism detection. arXiv preprint arXiv:2302.04335, 2023.
  11. Qin, C., et al., Is ChatGPT a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476, 2023.
  12. Hendy, A., et al., How good are gpt models at machine translation? a comprehensive evaluation. arXiv preprint arXiv:2302.09210, 2023.
  13. Rudolph, J., S. Tan, and S. Tan, ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 2023. 6(1).
  14. Bang, Y., et al., A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023, 2023.
  15. Tenhundfeld, N.L., Two Birds With One Stone: Writing a Paper Entitled “ChatGPT as a Tool for Studying Human-AI Interaction in the Wild” with ChatGPT.
  16. Wang, S. and P. Jin, A Brief Summary of Prompting in Using GPT Models. 2023.
  17. George, A.S. and A.H. George, A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 2023. 1(1): p. 9-23.
  18. Borji, A., A categorical archive of ChatGPT failures. arXiv preprint arXiv:2302.03494, 2023.
  19. Kalla, D. and N. Smith, Study and Analysis of Chat GPT and its Impact on Different Fields of Study. International Journal of Innovative Science and Research Technology, 2023. 8(3).
  20. Ray, P.P., ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 2023.
  21. Hariri, W., Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing. arXiv preprint arXiv:2304.02017, 2023.
  22. Sebastian, G., Do ChatGPT and Other AI Chatbots Pose a Cybersecurity Risk?: An Exploratory Study. International Journal of Security and Privacy in Pervasive Computing (IJSPPC), 2023. 15(1): p. 1-11.
  23. Pedreschi, D., et al. Meaningful explanations of black box AI decision systems. in Proceedings of the AAAI conference on artificial intelligence. 2019.
  24. Roose, K., GPT-4 is exciting and scary. The New York Times, 2023.
  25. Bubeck, S., et al., Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023.
  26. Radford, A., et al., Language models are unsupervised multitask learners. OpenAI blog, 2019. 1(8): p. 9.
  27. Brown, T., et al., Language models are few-shot learners. Advances in neural information processing systems, 2020. 33: p. 1877-1901.
  28. Raffel, C., et al., Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 2020. 21(1): p. 5485-5551.
  29. Bolukbasi, T., et al., Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems, 2016. 29.
  30. Caliskan, A., J.J. Bryson, and A. Narayanan, Semantics derived automatically from language corpora contain human-like biases. Science, 2017. 356(6334): p. 183-186.
  31. Blodgett, S.L., et al., Language (technology) is power: A critical survey of" bias" in nlp. arXiv preprint arXiv:2005.14050, 2020.
  32. Solaiman, I., et al., Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203, 2019.
  33. Hovy, D. and S. Prabhumoye, Five sources of bias in natural language processing. Language and Linguistics Compass, 2021. 15(8): p. e12432.
  34. Buolamwini, J. and T. Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. in Conference on fairness, accountability and transparency. 2018. PMLR.
  35. Bender, E.M. and B. Friedman, Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 2018. 6: p. 587-604.
  36. Kleinberg, J., S. Mullainathan, and M. Raghavan, Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807, 2016.
  37. Binns, R. Fairness in machine learning: Lessons from political philosophy. in Conference on fairness, accountability and transparency. 2018. PMLR.
  38. Prates, M.O., P.H. Avelar, and L.C. Lamb, Assessing gender bias in machine translation: a case study with google translate. Neural Computing and Applications, 2020. 32: p. 6363-6381.
  39. Kirk, H.R., et al., Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models. Advances in neural information processing systems, 2021. 34: p. 2611-2624.
  40. Bordia, S. and S.R. Bowman, Identifying and reducing gender bias in word-level language models. arXiv preprint arXiv:1904.03035, 2019.
  41. Johnson, M., et al., Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 2017. 5: p. 339-351.
  42. Pires, T., E. Schlinger, and D. Garrette, How multilingual is multilingual BERT? arXiv preprint arXiv:1906.01502, 2019.
  43. Dixon, L., et al. Measuring and mitigating unintended bias in text classification. in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 2018.
  44. McGee, R.W., Is chat gpt biased against conservatives? an empirical study. An Empirical Study (February 15, 2023), 2023.
  45. Carlini, N., et al. Extracting Training Data from Large Language Models. in USENIX Security Symposium. 2021.
  46. Ferrara, E., The history of digital spam. Communications of the ACM, 2019. 62(8): p. 82-91.
  47. Gao, L., et al., The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020.
  48. Gururangan, S., et al., Annotation artifacts in natural language inference data. arXiv preprint arXiv:1803.02324, 2018.
  49. Dev, S. and J. Phillips. Attenuating bias in word vectors. in The 22nd International Conference on Artificial Intelligence and Statistics. 2019. PMLR.
  50. Wei, J., et al., Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.
  51. Weidinger, L., et al. Taxonomy of risks posed by language models. in 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022.
  52. Deng, J. and Y. Lin, The Benefits and Challenges of ChatGPT: An Overview. Frontiers in Computing and Intelligent Systems, 2022. 2(2): p. 81-83.
  53. Brundage, M., et al., The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228, 2018.
  54. Ranade, P., et al. Generating fake cyber threat intelligence using transformer-based models. in 2021 International Joint Conference on Neural Networks (IJCNN). 2021. IEEE.
  55. Strubell, E., A. Ganesh, and A. McCallum, Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243, 2019.
  56. Adiwardana, D., et al., Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977, 2020.
  57. Howard, J. and S. Ruder, Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018.
  58. Hassani, H. and E.S. Silva, The role of ChatGPT in data science: how ai-assisted conversational interfaces are revolutionizing the field. Big data and cognitive computing, 2023. 7(2): p. 62.
  59. Rahaman, M., Can chatgpt be your friend? emergence of entrepreneurial research. Emergence of Entrepreneurial Research (February 18, 2023), 2023.
  60. Tlili, A., et al., What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 2023. 10(1): p. 15.
  61. Ali, S.R., et al., Using ChatGPT to write patient clinic letters. The Lancet Digital Health, 2023. 5(4): p. e179-e181.
  62. Sallam, M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. in Healthcare. 2023. MDPI.
  63. Kung, T.H., et al., Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS digital health, 2023. 2(2): p. e0000198.
  64. Khan, R.A., et al., ChatGPT-Reshaping medical education and clinical management. Pakistan Journal of Medical Sciences, 2023. 39(2): p. 605.
  65. Taecharungroj, V., “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 2023. 7(1): p. 35.
  66. Tiwary, N., Netizens, Academicians, and Information Professionals' Opinions About AI With Special Reference To ChatGPT. arXiv preprint arXiv:2302.07136, 2023.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Natural Language Processing (NLP) Large Language Model (LLM) ChatGPT.

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