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We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs).
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
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Fan, C., Huang, J.: Federated few-shot learning with adversarial learning. In: 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt). pp. 1–8. IEEE (2021)
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Patel, A., Bhattamishra, S., Goyal, N.: Are NLP models really able to solve simple math word problems? In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 2080–2094 (2021)
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Fan, C., Hu, J., Huang, J.: Private semi-supervised federated learning. In: IJCAI. pp. 2009–2015 (2022)
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Zaib, M., Zhang, W.E., Sheng, Q.Z., Mahmood, A., Zhang, Y.: Conversational question answering: A survey. Knowledge and Information Systems 64
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2023
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OpenAI: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)
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2022
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2022
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2022
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Wankhade, M., Rao, A.C.S., Kulkarni, C.: A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review 55
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2023
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2023
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Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models (2023)
2023
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