Comparison of Leading AI Models an Analytical Study of ChatGPT Google Bard and Microsoft Bing
Abstract This comparative analysis delves into the capabilities of three prominent Conversational AI models: ChatGPT, Google Bard, and Microsoft Bing Chat. The study encompasses a meticulous exploration of their conversational skills, natural language processing abilities, and creative text generation. Methodologically, this study crafts a comprehensive evaluation framework, including complexity levels and tasks for each dimension. Through user-generated responses, key metrics of fluency, coherence, relevance, accuracy, completeness, informativeness, creativity, and relevance were assessed. The results reveal distinctive strengths of the AI models. The theoretical implications lead to recommendations for dynamic learning, ethical considerations, and cross-cultural adaptability. Practically, avenues for future research were proposed, including real-time user feedback integration, multimodal capabilities exploration, and collaborative human-AI interaction studies. The analysis sets the stage for benchmarking and environmental impact assessments, underlining the need for standardized metrics.
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Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. Journal of the American Podiatry Association, 60(6). https://doi.org/10.1145/1553374.1553380
Biswas, R., & De, S. (2022). A comparative study on improving word embeddings beyond Word2Vec and GloVe. PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing. https://doi.org/10.1109/PDGC56933.2022.10053200
Borji, A., & Mohammadian, M. (2023). Battle of the wordsmiths: Comparing ChatGPT, GPT-4, Claude, and Bard. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4476855
Chen, X., Xie, H., & Tao, X. (2022). Vision, status, and research topics of Natural Language Processing. Natural Language Processing Journal, 1, 100001. https://doi.org/10.1016/j.nlp.2022.100001
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., … Fiedel, N. (2022). PaLM: Scaling language modeling with pathways. http://arxiv.org/abs/2204.02311
DivyaSingh456. (2019, May 2). Evolution of chatbots & their performance. DataScienceCentral.Com. https://www.datasciencecentral.com/evolution-of-chatbots-amp-their-performance/
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. http://arxiv.org/abs/1412.6572
Gu, J., Han, Z., Chen, S., Beirami, A., He, B., Zhang, G.,… & Torr, P. (2023). A systematic survey of prompt engineering on vision-language foundation models. arXiv preprint arXiv:2307.12980.
Gupta, A., Hathwar, D., & Vijayakumar, A. (n.d.). Introduction to AI chatbots. www.ijert.org
Kim, T. H. (2010). Emerging approach of natural language processing in opinion mining: A review. Communications in Computer and Information Science, 75 CCIS. https://doi.org/10.1007/978-3-642-13467-8_12
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. http://arxiv.org/abs/1412.6980
Koubaa, A., Boulila, W., Ghouti, L., Alzahem, A., & Latif, S. (2023). Exploring ChatGPT capabilities and limitations: A survey. IEEE Access, 11, 118698–118721. https://doi.org/10.1109/ACCESS.2023.3326474
Kovan, M., & Márta, T.-S. (2023). Chatbot development using APIs and integration into the MOOC. Journal of New Technologies in Research, 5(1).
Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S. F., Forshee, R., Walderhaug, M., & Botsis, T. (2017). Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of Biomedical Informatics, 73. https://doi.org/10.1016/j.jbi.2017.07.012
OpenAI. (2022, November 20). Introducing ChatGPT. OpenAI. https://openai.com/blog/chatgpt
Oppenlaender, J., Linder, R., & Silvennoinen, J. (2023). Prompting AI art: An investigation into the creative skill of prompt engineering. http://arxiv.org/abs/2303.13534
Pons, E., Braun, L. M. M., Hunink, M. G. M., & Kors, J. A. (2016). Natural language processing in radiology: A systematic review. Radiology, 279(2). https://doi.org/10.1148/radiol.16142770
Psyarxiv Manuscript. (n.d.).
Qin, R., Huang, M., Liu, J., & Miao, Q. (2022). Hybrid attention-based transformer for long-range document classification. Proceedings of the International Joint Conference on Neural Networks, 2022-July. https://doi.org/10.1109/IJCNN55064.2022.9891918
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. KeAi Communications Co. https://doi.org/10.1016/j.iotcps.2023.04.003
Samant, R. M., Bachute, M. R., Gite, S., & Kotecha, K. (2022). Framework for deep learning-based language models using multi-task learning in natural language understanding: A systematic literature review and future directions. IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3149798
Srivastava, N., Hinton, G., Krizhevsky, A., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15.
Statista (2023, August). Natural language processing - Global | Market forecast. Statista. https://www.statista.com/outlook/tmo/artificial-intelligence/natural-language-processing/worldwide#market-size
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 2017-December.
Velásquez-Henao, J. D., Franco-Cardona, C. J., & Cadavid-Higuita, L. (2023). Prompt engineering: A methodology for optimizing interactions with AI-language models in the field of engineering. DYNA, 90(230), 9-17. https://doi.org/10.15446/dyna.v90n230.111700
Wang, J., Shi, E., Yu, S., Wu, Z., Ma, C., Dai, H., Yang, Q., Kang, Y., Wu, J., Hu, H., Yue, C., Zhang, H., Liu, Y., Li, X., Ge, B., Zhu, D., Yuan, Y., Shen, D., Liu, T., & Zhang, S. (2023). Prompt engineering for healthcare: Methodologies and applications. http://arxiv.org/abs/2304.14670
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136. https://doi.org/10.1109/JAS.2023.123618
Adomako, P., Khan, T. A., Ali, R. H., & Koutaly, R. (2025). Comparison of Leading AI Models an Analytical Study of ChatGPT Google Bard and Microsoft Bing. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e31857. https://doi.org/10.14201/adcaij.31857
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