新闻
It's Not RoPE that Creates Sinks: The Role of Self-Concentration and Value-Non-Mixing in Attention
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers identified that attention sinks in LLMs stem from self-concentration and value-non-mixing, not from RoPE positional encoding as previously assumed.
- 为何重要
- Matters for engineers optimizing LLM quantization and those debugging unexpected activation patterns at sequence start positions.
- 注意
- Paper is recent and accepted but not yet peer-reviewed in final form; findings are empirical and may not generalize across all model architectures.
- llm
- language model
- quantiz
- attention
- token
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