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English Information

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Authors
# Name
1 André Regino(andre.regino@students.ic.unicamp.br)
2 Victor Hochgreb(victor@gobots.com.br)
3 Julio Dos Reis(jreis@ic.unicamp.br)

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Reference
# Reference
1 Arora, S., Narayan, A., Chen, M. F., Orr, L. J., Guha, N., Bhatia, K., Chami, I., Sala, F., and Ré, C. (2022). Ask me anything: A simple strategy for prompting language models. arXiv preprint arXiv:2210.02441.
2 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
3 Chen, J., Ma, L., Li, X., Thakurdesai, N., Xu, J., Cho, J. H., Nag, K., Korpeoglu, E., Kumar, S., and Achan, K. (2023). Knowledge graph completion models are few-shot learners: An empirical study of relation labeling in e-commerce with llms. arXiv preprint arXiv:2305.09858.
4 Cheng, X., Bao, Y., Zarifis, A., Gong, W., and Mou, J. (2021). Exploring consumers’ response to text-based chatbots in e-commerce: the moderating role of task complexity and chatbot disclosure. Internet Research, 32(2):496–517.
5 dos Santos Viriato, P. J., de Souza, R. R., Villas, L. A., and dos Reis, J. C. (2023). Reveal- ing chatbot humanization impact factors. In Kurosu, M. and Hashizume, A., editors, Human-Computer Interaction - Thematic Area, HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23-28, 2023, Proceedings, Part III, volume 14013 of Lecture Notes in Computer Science, pages 294–313. Springer.
6 Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023). Mistral 7b. arXiv preprint arXiv:2310.06825.
7 Legrand, G., Rodrigues, A., and Gama, J. (1991). Dicionário de filosofia.
8 Li, M. and Wang, R. (2023). Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. Journal of Retailing and Consumer Services, 71:103209
9 Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Li, X., Zhu, C., Guo, H., Yu, Y., Tang, R., et al. (2023). How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817.
10 Regino, A. G., Caus, R. O., Hochgreb, V., and Reis, J. C. d. (2023). Leveraging knowledge graphs for e-commerce product recommendations. SN Computer Science, 4(5):689.
11 Sant’Anna, D. T., Caus, R. O., dos Santos Ramos, L., Hochgreb, V., and dos Reis, J. C. (2020). Generating knowledge graphs from unstructured texts: Experiences in the e- commerce field for question answering. In Advances in Semantics and Linked Data: Joint Workshop Proceedings from ISWC 2020, pages 56–71.
12 Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ili´c, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al. (2022). Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100
13 Tsai, W.-H. S. and Chuan, C.-H. (2023). Humanizing chatbots for interactive marketing. The Palgrave handbook of interactive marketing, pages 255–273