新闻
Distance generalization in transformers: why bother with positional encoding?
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers tested whether positional encoding schemes like RoPE and ALiBi help transformers generalize when token distances change between training and inference.
- 为何重要
- Matters for engineers building transformers that must handle variable spacing or gaps in token sequences beyond training distribution.
- 注意
- Study uses only synthetic delay copy tasks; findings may not transfer to real language or production transformer behavior in practice.
- inference
- token
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