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Short description of portfolio item number 2
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.
Published in IJCNN25 Arxiv, 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. The paper was accepted for presentation at IJCNN 2025.
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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