Prompt Engineering in Conversational Assistants for Self-Care Promotion based on Large Language Models
Abstract
The implementation of LLM assistants brings challenges, including ensuring safe and informative interactions. This article explores the use of prompt engineering in creating MarIA, a virtual assistant based on GPT-3.5 for patients with diabetes, aiming to promote self-care. Prompt engineering sought dialogue empathy and a personalized dialogue style with accurate self-care information. An experiment with 35 patients was carried out and user interactions when using MarIA versions were analyzed. The information exchanged showed differences in engagement rates, demonstrating the effectiveness of customization. They also demonstrated efficient and safe dialogues, avoiding frustrations or risks.
References
Balsa, J., Félix, I., Cláudio, A. P., Carmo, M. B., Silva, I. C. E., Guerreiro, A., Guedes, M., Henriques, A., & Guerreiro, M. P. (2020). Usability of an Intelligent Virtual Assistant for Promoting Behavior Change and Self-Care in Older People with Type 2 Diabetes. Journal of medical systems, 44(7), 130.
Bodenheimer, T., Davis, C., and Holman, H. (2007). Helping Patients Adopt Healthier Behaviors. Clinical Diabetes, 25(2), 66-70.
Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., and Weston, J. (2023). Chain-of-verification reduces hallucination in large language models.
Furtado, E. S., Furtado, L. S. (2023). Como ensinar Aspectos de IHC de forma desplugada? O uso de um espaço cultural como metáfora de interação. Revista Brasileira de Informática na Educação, [S. l.], v. 31, p. 488–510, 2023.
Goh, E., Bunning, B., Khoong, E., Gallo, R., Milstein, A., Centola, D., & Chen, J. H. (2023). ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes. medRxiv : the preprint server for health sciences, 2023.11.24.23298844.
Heisler, M. and Resnicow, K. (2008). Helping Patients Make and Sustain Healthy Changes: A Brief Introduction to Motivational Interviewing in Clinical Diabetes Care. Clinical Diabetes, 26(4), 161–165.
Huang, M., Zhu, X., and Gao, J. (2020). Challenges in Building Intelligent Open-Domain Dialog Systems. ACM Transactions on Information Systems (TOIS), 38(3), 1-32. arXiv:1905.05709v3.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. arXiv:2107.13586
Muñoz, D., Pedell, S., and Sterling, L. (2022). Understanding Confidence of Older Adults for Embracing Mobile Technologies. In Proceedings of the 34th Australian Conference on Human-Computer Interaction (pp. 38-50).
Nikitina, S., Callaioli, A., and Baez, M. (2018). Smart conversational agents for reminiscence. In 1st Workshop on Software Engineering for Cognitive Services (pp. 52–57). ACM, NY, USA.
Nigh, M. (2023). ChatGPT3 Prompt Engineering. GitHub. Retrieved from [link].
Paradigmxyz/flux (2023). Flux: Graph-based LLM power tool for exploring many completions in parallel. Retrieved from [link]
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv:2204.06125, 1(2), 3.
Sharma, D. Kaushal, S. Kumar, H. and Gainder, S. (2022) "Chatbots in Healthcare: Challenges, Technologies and Applications," 4th International Conference on Artificial Intelligence and Speech Technology (AIST), Delhi, India, 2022, pp. 1-6, DOI: 10.1109/AIST55798.2022.10065328.
Teo, S. (2023). How I Won Singapore’s GPT-4 Prompt Engineering Competition: A deep dive into the strategies I learned for harnessing the power of Large Language Models (LLMs). Towards Data Science. Retrieved from [link]
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2023). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Proceedings of the 36th Conference on Neural Information Processing Systems, 35, 24824-24837. arXiv:2201.11903v6.
Bodenheimer, T., Davis, C., and Holman, H. (2007). Helping Patients Adopt Healthier Behaviors. Clinical Diabetes, 25(2), 66-70.
Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., and Weston, J. (2023). Chain-of-verification reduces hallucination in large language models.
Furtado, E. S., Furtado, L. S. (2023). Como ensinar Aspectos de IHC de forma desplugada? O uso de um espaço cultural como metáfora de interação. Revista Brasileira de Informática na Educação, [S. l.], v. 31, p. 488–510, 2023.
Goh, E., Bunning, B., Khoong, E., Gallo, R., Milstein, A., Centola, D., & Chen, J. H. (2023). ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes. medRxiv : the preprint server for health sciences, 2023.11.24.23298844.
Heisler, M. and Resnicow, K. (2008). Helping Patients Make and Sustain Healthy Changes: A Brief Introduction to Motivational Interviewing in Clinical Diabetes Care. Clinical Diabetes, 26(4), 161–165.
Huang, M., Zhu, X., and Gao, J. (2020). Challenges in Building Intelligent Open-Domain Dialog Systems. ACM Transactions on Information Systems (TOIS), 38(3), 1-32. arXiv:1905.05709v3.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. arXiv:2107.13586
Muñoz, D., Pedell, S., and Sterling, L. (2022). Understanding Confidence of Older Adults for Embracing Mobile Technologies. In Proceedings of the 34th Australian Conference on Human-Computer Interaction (pp. 38-50).
Nikitina, S., Callaioli, A., and Baez, M. (2018). Smart conversational agents for reminiscence. In 1st Workshop on Software Engineering for Cognitive Services (pp. 52–57). ACM, NY, USA.
Nigh, M. (2023). ChatGPT3 Prompt Engineering. GitHub. Retrieved from [link].
Paradigmxyz/flux (2023). Flux: Graph-based LLM power tool for exploring many completions in parallel. Retrieved from [link]
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv:2204.06125, 1(2), 3.
Sharma, D. Kaushal, S. Kumar, H. and Gainder, S. (2022) "Chatbots in Healthcare: Challenges, Technologies and Applications," 4th International Conference on Artificial Intelligence and Speech Technology (AIST), Delhi, India, 2022, pp. 1-6, DOI: 10.1109/AIST55798.2022.10065328.
Teo, S. (2023). How I Won Singapore’s GPT-4 Prompt Engineering Competition: A deep dive into the strategies I learned for harnessing the power of Large Language Models (LLMs). Towards Data Science. Retrieved from [link]
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2023). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Proceedings of the 36th Conference on Neural Information Processing Systems, 35, 24824-24837. arXiv:2201.11903v6.
Published
2024-06-25
How to Cite
SILVA, Vitória; FURTADO, Elizabeth Sucupira; OLIVEIRA, Juliana; FURTADO, Vasco.
Prompt Engineering in Conversational Assistants for Self-Care Promotion based on Large Language Models. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 377-388.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.2252.
