Humanizing Answers for Compatibility Questions in E-commerce using Large Language Models
Resumo
Customer experience is a critical aspect of online purchase decisions. The service, the attendant's response, and how the customer is treated contribute to customer satisfaction. This article investigates using large language models for humanizing customer support in e-commerce. In particular, we address compatibility questions. Leveraging the infrastructure and dataset from an AI Brazilian startup, we compare the effectiveness of three different models to generate natural language answers in Portuguese. We generate human-like answers and evaluate them based on compatibility correctness, number of tokens, legibility, human likeness, and effect on the purchase. Our results highlight the effectiveness and drawbacks of the explored models in different temperature settings. This study improves customer experiences and provides guidance for e-commerce platforms in implementing humanized responses.
Palavras-chave:
text humanization, natural language processing, electronic commerce
Referências
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.
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.
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.
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.
dos Santos Viriato, P. J., de Souza, R. R., Villas, L. A., and dos Reis, J. C. (2023). Revealing 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.
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.
Legrand, G., Rodrigues, A., and Gama, J. (1991). Dicionário de filosofia.
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.
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.
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.
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.
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, 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.
Tsai, W.-H. S. and Chuan, C.-H. (2023). Humanizing chatbots for interactive marketing. The Palgrave handbook of interactive marketing, pages 255–273.
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.
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.
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.
dos Santos Viriato, P. J., de Souza, R. R., Villas, L. A., and dos Reis, J. C. (2023). Revealing 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.
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.
Legrand, G., Rodrigues, A., and Gama, J. (1991). Dicionário de filosofia.
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.
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.
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.
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.
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, 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.
Tsai, W.-H. S. and Chuan, C.-H. (2023). Humanizing chatbots for interactive marketing. The Palgrave handbook of interactive marketing, pages 255–273.
Publicado
14/10/2024
Como Citar
REGINO, André Gomes; HOCHGREB, Victor; DOS REIS, Julio Cesar.
Humanizing Answers for Compatibility Questions in E-commerce using Large Language Models. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 300-312.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240657.
