5 accepted papers
In this work, we present the first systematic evaluation of catastrophic forgetting and modality inequivalence in speech large language models, showing that introducing speech capabilities can degrade knowledge and reasoning even when inputs remain textual, and performance further decreases with spo…
Recent advances in large language models (LLMs) have shown remarkable capabilities across textual and multimodal domains. In parallel, large language diffusion models have emerged as a promising alternative to the autoregressive paradigm, offering improved controllability, bidirectional context mode
Deep learning models for medical disease detection require extensive labeled data, which is often scarce and expensive. Transfer learning can help by leveraging knowledge from large source domains, but directly fine-tuning these models can lead to catastrophic forgetting, making it impossible to reu…
Continual learning aims to learn new tasks sequentially without forgetting previously acquired knowledge. However, catastrophic forgetting remains a significant challenge. In this paper, we introduce kNN-CL, a simple yet effective approach that harnesses k-nearest neighbors (kNN) to mitigate forgett…
Modern large language models (LLMs) exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities. This versatility allows them to simulate complex human-like interactions and behaviors within various contexts, as well as to emulate spe…