新闻
On the Fundamental Limits of Prefill-Based Decoding Length Prediction
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers show that predicting LLM output length from prefill-phase states has fundamental limits tied to autoregressive decoding's inherent randomness.
- 为何重要
- Matters for inference scheduling engineers optimizing LLM serving latency and resource allocation across variable-length outputs.
- 注意
- Current benchmarks collapse when model, dataset, or sampling changes. Predictor accuracy is bounded by system error, not model design alone.
- llm
- language model
- inference
- token
- benchmark
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