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cs.CL2025
Do we really have to filter out random noise in pre-training data for language models?
Jinghan Ru, Yuxin Xie, Xianwei Zhuang +5
Web-scale pre-training datasets are the cornerstone of LLMs' success. However, text data curated from the Internet inevitably contains random noise caused by decoding errors or unr…
cs.CL2024
Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning
Xuxin Cheng, Wanshi Xu, Zhihong Zhu +2
Spoken language understanding (SLU) is a core task in task-oriented dialogue systems, which aims at understanding the user's current goal through constructing semantic frames. SLU…
cs.CL2024★ 1 cited
Retrieval is Accurate Generation
Bowen Cao, Deng Cai, Leyang Cui +4
Standard language models generate text by selecting tokens from a fixed, finite, and standalone vocabulary. We introduce a novel method that selects context-aware phrases from a co…