Published in Journal of King Saud University Computer and Information Sciences, 2026
AdaCOS makes federated learning training dynamic and smart. Instead of treating every device the same, it constantly evaluates their updates. For devices contributing valuable, on-target information, it intelligently increases their communication allowance while reducing protective noise.
Published in International Joint Conference on Neural Networks (IJCNN), 2025
Developed MMSQL, a test suite that evaluates how well LLMs manage different question types and multi-turn interactions. Additionally, created a multi-agent system to better identify question types and select appropriate strategies. Experiments show that this approach enhances the models' ability to navigate conversational complexities. For a more detailed presentation, refer to the Page. IEEE Arxiv
Published in Arxiv, 2024
Fine-tuning LLMs for Text-to-SQL tasks is effective, but they often struggle with multi-turn queries due to ambiguity. QDA-SQL, a data augmentation method that generates diverse multi-turn Q&A pairs using LLMs. Fine-tuning with QDA-SQL improves SQL statement accuracy and enhances the models' ability to manage challenging, unanswerable questions. The generation script and test set are released at Github.