In-Place Tokenizer Expansion for Pre-trained LLMs
Score
74提出了新颖的原地分词器扩展方法，解决了端侧模型多语言部署中词表扩容的实际痛点。
方法描述清晰，实验设计合理，附有负面发现，论证充分。
该方法可直接用于已有模型的优化，开源权重和分词器，实用价值高。
虽为arXiv最新论文，但方法本身并非突破性创新，而是工程优化。
来自学术界正规论文，可复现，信源可信。
Base 82.5 × T2 Source weight ×0.9 = 74
Curation line 70Selected
Why selected
The method is practical with significant results and thorough experiments, but generalization to extremely low-resource languages is not verified.
AI commentary
「This paper offers a highly practical engineering solution for multilingual on-device deployment, especially valuable for teams concerned with inference cost and language coverage. The method is simple and effective, and the open-source release lowers the barrier to reproduction. While incremental, it addresses a real pain point with significant impact.」
Quick overview
A tokenizer fixed during pre-training allocates vocabulary proportionally to the training corpus. When priorities shift, later-added languages suffer from more tokens per word, increasing latency, compute, and energy. Cloud models can afford large vocabularies, but on-device models use small vocabularies due to bandwidth constraints, causing fragmentation for non-default languages. This paper introduces an in-place tokenizer expansion method: continue BPE merges on a multilingual corpus, keep source tokens unchanged, and initialize new token embeddings as the mean of their source sub-token embeddings. A two-stage adaptation (embedding-only then full-model continued pre-training) recovers source checkpoint quality. Applied to LFM2-8B-A1B, it reduces tokens for Hindi and Vietnamese by ~2.4× and 2.6× (up to 4.0× for Thai), with estimated 2.2-3.7× per-character decode speedup. Model weights and the expanded tokenizer are released.
- Proposes an in-place tokenizer expansion method that preserves source tokens and initializes new token embeddings as mean of their source sub-token embeddings.
- Two-stage adaptation: embedding-only training followed by full-model continued pre-training recovers source checkpoint quality.
- Applied to LFM2-8B-A1B (8B MoE model), expands to 128K vocabulary, reducing tokens for Hindi and Vietnamese by 2.4× and 2.6× (up to 4.0× for Thai).
- Estimated 2.2-3.7× per-character decode speedup on reference devices; model weights and expanded tokenizer are open-sourced.
Full text
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