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cs.CL2024★ 1 cited
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko +11
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The an…
cs.DC2024
Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
Wei Ji, Li Li, Zheqi Lv +5
In our increasingly interconnected world, where intelligent devices continually amass copious personalized multi-modal data, a pressing need arises to deliver high-quality, persona…
cs.HC2024
Evaluating Saliency Explanations in NLP by Crowdsourcing
Xiaotian Lu, Jiyi Li, Zhen Wan +3
Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to expla…