FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
Abstract
Recent advancements in AI have made LLMs valuable tools for automating the interpretation of textual descriptions of business processes and for converting formal process specifications into natural language. However, there are no practical methodologies or systematic assessments to ensure these automatic translations are faithful. This paper proposes a novel approach, based on an auxiliary bidirectional translation task, to assess LLMs performance quantitatively; also, it also empirically evaluates the performance of state-of-the-art LLMs for bidirectional translations between natural language and declarative formal process specifications. The results reveal substantial variability in performance among the LLMs, highlighting the importance of LLM selection and confirming the need for a robust method for assessing LLMs' outputs.
Cite
Text
Li et al. "FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/501Markdown
[Li et al. "FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/li2024ijcai-fedconpe/) doi:10.24963/ijcai.2024/501BibTeX
@inproceedings{li2024ijcai-fedconpe,
title = {{FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients}},
author = {Li, Zhuohua and Liu, Maoli and Lui, John C. S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4533-4541},
doi = {10.24963/ijcai.2024/501},
url = {https://mlanthology.org/ijcai/2024/li2024ijcai-fedconpe/}
}