English(EN) Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing

LLM处理顺序显著影响多实例任务准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一篇新的arXiv论文研究了输入实例的顺序如何影响大型语言模型（LLM）在多实例处理（MIP）任务中的性能。研究人员发现，LLM对实例顺序非常敏感，准确性根据输入的排列方式变化高达90个百分点。研究表明，当多数标签实例出现在序列的早期时，模型的准确性往往更高，并且对推理轨迹和归因分数的分析表明，不同输入位置的侧重点不均。 AI

影响突出了LLM聚合任务中潜在的偏差，表明需要仔细安排输入顺序或进行模型调整，以实现可靠的多实例处理。

排序理由关于LLM处理行为的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jingxuan Chen, Mohammad Taher Pilehvar, Jose Camacho-Collados · 2026-10-23 04:00

Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing

arXiv:2610.29057v1 Announce Type: new Abstract: Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated answer. This setting requires multi-instance process…

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Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing

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