An RL framework uses digital twin representations with hierarchical uncertainty estimates and a novel clinical plausibility reward to train LLMs for surgical VideoQA, achieving SOTA on a new 2000-pair benchmark and two existing datasets.
Neural-symbolic videoqa: Learning compositional spatio-temporal reasoning for real-world video question answering.arXiv preprint arXiv:2404.04007, 2024
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Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
An RL framework uses digital twin representations with hierarchical uncertainty estimates and a novel clinical plausibility reward to train LLMs for surgical VideoQA, achieving SOTA on a new 2000-pair benchmark and two existing datasets.