Unverified50% confidenceFactExact time
研究发现Lost in the Middle衰减在上下文长度超过4K token后急剧加速
1
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50%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
Unverified研究发现位于上下文首尾的信息被模型召回的概率远高于中间位置，衰减在上下文长度超过4K token后急剧加速76% similarUnverifiedLost in the Middle 效应指模型对位于极长输入序列中间位置的信息关注度会系统性下降，在超过 20K token 时开始显著，100K token 级别尤为突出69% similarUnverifiedThe quality of long-context understanding in small models degrades rapidly with length, typically deteriorating after exceeding a 4K–8K token window.66% similarUnverifiedLLMs exhibit a "Lost in the Middle" phenomenon when processing ultra-long contexts, where the effective recall rate of information in the middle segment may drop by more than 40%.66% similar
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