Beyond N-gram: Data-Aware X-GRAM Extraction for Efficient Embedding Parameter Scaling
Large token-indexed lookup tables provide a compute-decoupled scaling path, but their practical gains are often limited by poor parameter efficiency and rapid memory growth. We attribute these limitations to Zipfian under-training of the long tail, heterogeneous demand across layers, and "slot collapse" that produces redundant embeddings. To address this, we propose X-GRAM, a frequency-aware dynamic token-injection framework. X-GRAM employs hybrid hashing and alias mixing to compress the tail while preserving head capacity, and refines retrieved vectors via normalized SwiGLU ShortConv to extract diverse local n-gram features. These signals are integrated into attention value streams and i...
| Authors | Yilong Chen, Yanxi Xie, Zitian Gao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 24 April 2026 |
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