Unverified50% confidenceFactExact time
The paper "Found in the Middle" (Hsieh et al., ACL Findings 2024) attributes the lost-in-the-middle effect to the attention mechanism: tokens at the beginning and end of the input receive more attention regardless of their actual relevance.
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50%
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Long-term
Relevance
10/12/2026
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UnverifiedA 2023 Stanford University paper titled 'Lost in the Middle' found that models pay significantly less attention to information in the middle of the context window than at the beginning and end, exhibiting a U-shaped attention distribution.77% similarVerified研究表明当上下文过长时，模型对中间部分信息的注意力会显著下降，即"Lost in the Middle"现象74% similarUnverifiedCurrent frontier models face attention decay when handling long-range dependencies, with the ability to maintain consistency of early-established settings declining significantly as context length increases.74% similarUnverified当前大模型并没有真正的持久化记忆机制，随着对话轮次增加，早期需求描述在注意力机制中的权重会逐渐衰减72% similar
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