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2024相册

大二下大三上

2025相册

大三下大四上

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

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.

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Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

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

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AdaCOS: adaptive differential privacy shuffle model based on cosine similarity

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.

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Conference Proceeding talk on MMSQL testsuit

Published: June 01, 2025

presentation of paper “Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types” Bilibili

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.