新闻
RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- RiLM replaces the output layer in tiny language models by treating token prediction as geodesic distance on a Riemannian manifold, cutting parameters by roughly one third.
- 为何重要
- Matters for engineers deploying models under one million parameters on edge devices or building domain-specific models with strict resource constraints.
- 注意
- Results are limited to controlled small-model comparisons with vocabularies up to ten thousand tokens, not full-scale production scenarios or modern vocabulary sizes.
- language model
- embedding
- token
- edge
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