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PsPLUG is proposed, a lightweight plug-in that learns a user-specific residual after accounting for the requested style, and better preserves user preferences while providing precise control over the balance between personalization and style adherence.
This work presents CARD, a hierarchical framework that achieves effective personalization through progressive refinement that first clusters users according to shared stylistic patterns and learns group-specific LoRA adapters, enabling robust generalization and strong low-resource performance.
Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course toward artificial general intelligence and emerging as a central issue in the global technological race. This article conducts a comprehensive...
This work introduces audio multi-perspective clustering, where a model directly partitions speech recordings according to a natural-language perspective while inferring both the number of clusters and their assignments.
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