MinGram is a simplified Unigram tokenizer training method that prioritizes token count minimization to deliver higher compression than BPE and standard Unigram while retaining competitive morphological alignment and superior bits-per-byte performance in language model training.
Too Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications
3 Pith papers cite this work. Polarity classification is still indexing.
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Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
MLLM-Microscope measures linearity, dimension and anisotropy of multimodal token streams in LLaVA-NeXT and OmniFusion, reporting high linearity overall and model-specific differences tied to modality fusion.
citing papers explorer
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Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.