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McGill University
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Fine-tuning LLMs with a data-driven pipeline that incorporates real user queries and a new augmentation method (AugFC) dramatically improves function calling performance in online financial QA systems.
Forget retraining: ReAd dynamically adapts deployed sequential recommendation models to real-time preference shifts by retrieving and integrating collaborative user preference signals at test time.
