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This paper shows that sequence-pooled normalization in convolutional networks provides global context beyond the receptive field, supplying most of the context needed for sequence labeling and affecting attribution in network ablation studies.
This paper investigates whether Arabic character form-function relationships are arbitrary from an NLP perspective, showing that random character remappings to reduced rasm sets can achieve competitive performance across various NLP tasks.
DIRECT is a framework for sequence labeling using large language models that improves domain alignment through Direct Preference Optimization (DPO) after supervised fine-tuning and increases inference efficiency via controlled decoding with template-filling and KV cache reuse.
This paper introduces a temporal multi-signal fusion method for detecting token-level hallucinations in language models using sequence labeling over fused features, achieving improved cross-model performance without access to model internals.