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ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, audio, and dialogue as input, provides a feasible path for large-scale, evidence-based early ASD screening in clinical practice.
Unlearning in federated systems can now be achieved in seconds without sacrificing model integrity, thanks to a novel filter-based approach.
LLMs can learn to synthesize data more effectively by accumulating and transferring experience across a stream of sequential synthesis tasks, opening the door to more efficient and adaptable synthetic data generation.
