Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 2 2026 04 01 Dynamic Retrieval-Based Prompting for Cross-Lingual Dialogue Understanding in Persian 183 196 3727 10.22044/jadm.2025.16583.2785 EN Saedeh Tahery Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran 0000-0001-9373-9629 Saeed Farzi Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran 0000-0003-2850-0616 Journal Article 2025 07 27 Dialogue understanding for low-resource languages like Persian remains challenging due to limited annotated data, which constrains supervised training at scale. We propose a simple yet effective training-free method that combines machine translation, retrieval-based example selection, and prompting with a large language model (GPT-4o) to improve zero-shot cross-lingual performance. Given a Persian utterance translated into English, our method retrieves semantically and lexically similar English examples using a hybrid similarity function, translates them back into Persian, and constructs a few-shot prompt tailored to the input. This input-sensitive strategy enhances the quality of the examples, helping the model align more effectively with each instance. Experimental results on the Persian-ATIS dataset show that our approach improves intent detection and achieves competitive slot filling performance, outperforming state-of-the-art baselines without requiring any supervision in the target language. The modular pipeline is easy to reproduce and, in future work, can be extended to other low-resource languages, tasks, or retrieval configurations. The repository of our work is available at https://anonymous.4open.science/r/Persian_Language_Understanding-FDF4. https://jad.shahroodut.ac.ir/article_3727_095a4dfc02cf04717269a387e1f7d836.pdf