| Texto completo | |
| Autor(es): |
Cabrera, Eduardo Faria
;
de Barros, Marcel Rodrigues
;
Reali Costa, Anna Helena
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTELLIGENT SYSTEMS, BRACIS 2024, PT II; v. 15413, p. 15-pg., 2025-01-01. |
| Resumo | |
Large Language Models (LLMs) have shown remarkable capabilities across various applications but struggle with sequential decision-making tasks like planning. This work demonstrates that integrating LLMs with Finite-State Machines (FSMs) can enhance their reasoning and planning abilities, while also offering increased reliability. Several setup variants are compared, providing a better understanding of how validations, feedback loops, and restrictions enhance robustness and effectiveness. A comparison to the well known Chain-of-Thoughts approach is also provided. Our methods improve planning capabilities of all analysed LLMs, consistently increasing the success rate in solving tasks of varying complexities. A detailed analysis of the two best variants are provided, highlighting their respective strengths and weaknesses. (AU) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada |