embedding initialization 1model adaptation 1multilingual tokenization 1pretrained language models 1tokenizer expansion 1
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cs.CL2026
In-Place Tokenizer Expansion for Pre-trained LLMs
Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera +7
The paper proposes an in‑place tokenizer expansion method that continues a pre‑trained model’s BPE merges on multilingual data, reuses existing token embeddings, and initializes ne…
cs.CL2024
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
Thomas Gauthier-Caron, Shamane Siriwardhana, Elliot Stein +5
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retrai…