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Getting the most out of your tokenizer for pre-training and domain adaptation
Tokenization is an understudied and often neglected component of modern LLMs. Most
published works use a single tokenizer for all experiments, often borrowed from another …
published works use a single tokenizer for all experiments, often borrowed from another …
Performance, energy consumption and costs: a comparative analysis of automatic text classification approaches in the Legal domain
L Rigutini, A Globo, M Stefanelli… - INTERNATIONAL …, 2024 - usiena-air.unisi.it
The common practice in Machine Learning research is to evaluate the top-performing
models based on their performance. However, this often leads to overlooking other crucial …
models based on their performance. However, this often leads to overlooking other crucial …
Generation with Dynamic Vocabulary
We introduce a new dynamic vocabulary for language models. It can involve arbitrary text
spans during generation. These text spans act as basic generation bricks, akin to tokens in …
spans during generation. These text spans act as basic generation bricks, akin to tokens in …
Efficient Online Inference of Vision Transformers by Training-Free Tokenization
The cost of deploying vision transformers increasingly represents a barrier to wider industrial
adoption. Existing compression requires additional end-to-end fine-tuning or incurs a …
adoption. Existing compression requires additional end-to-end fine-tuning or incurs a …
MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies
Tokenization is fundamental to Natural Language Processing (NLP), directly impacting
model efficiency and linguistic fidelity. While Byte Pair Encoding (BPE) is widely used in …
model efficiency and linguistic fidelity. While Byte Pair Encoding (BPE) is widely used in …