English(EN) I tried to reproduce “lost in the middle” on current models and actually couldn’t do that! [P]

用户发现当前LLM在简单检索任务中不存在“中间遗忘”现象

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 09:22

一位用户尝试在当前的大型语言模型（特别是Anthropic的Haiku 4.5和Sonnet 5）上复现“中间遗忘”（lost in the middle）现象。用户发现，在最多30份文档的情况下，模型在检索单一事实时并未表现出明显的位置敏感性。唯一观察到的变化是答案的格式（例如，“3”与“three”），用户并未将其计为事实错误。用户承认这是一个简化的测试用例，该现象可能仍会出现在更长的文档或更复杂的任务中。 AI

影响表明当前LLM在简单任务的检索中可能比之前认为的更能抵抗位置偏差。

排序理由用户实验和对LLM行为的评论，而非主要发布或研究论文。

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Commentary; 用户实验和对LLM行为的评论，而非主要发布或研究论文。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

r/MachineLearning TIER_1 English(EN) · /u/Beneficial_Use2116 · 2026-11-09 09:22

我尝试在当前模型上复现“迷失中间”现象，但实际上未能成功！[P]

<div class="md"><p>Everyone keeps citing lost in the middle so, instead of going by the paper's citations, i wanted to actually check it on my own stuff, without building a labeled set for it.</p> <p>So i did the lazy version. make up facts the model can’t already …

本文如何被排名

报道来源 [1]

我尝试在当前模型上复现“迷失中间”现象，但实际上未能成功！[P]

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