English(EN) SELD: Detecting Silent Evidence Loss in Context Compression beyond the Answer

新的SELD方法检测语言模型上下文压缩中的静默证据丢失

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了SELD（Silent Evidence Loss Detector，静默证据丢失检测器），一种新颖的方法，用于识别语言模型上下文压缩过程中文本中的关键证据何时被移除，即使模型的最终答案保持不变。这种静默证据丢失可能导致答案缺乏支持，并在被其他模型复用时带来风险。SELD无需训练或标签即可运行，通过分析压缩前后阅读器模型对未陈述答案的偏好变化。使用新的SEL-Pairs基准进行的实验表明，SELD能够有效检测各种模型和压缩率下的证据丢失，其性能优于现有方法。 AI

影响这种新的检测方法可以通过确保压缩后的上下文保留必要的支持证据来提高语言模型的可靠性。

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arXiv cs.AI TIER_1 English(EN) · Wentao Lei, Li Liu · 2026-11-11 05:00

SELD：检测上下文压缩中答案之外的静默证据丢失

arXiv:2611.12903v1 Announce Type: cross Abstract: Context compression is now routine for language models with long inputs, but a compressed context is useful only if it keeps the evidence for the answer. Existing checks compare the reader model's answer or its most likely words, …

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SELD：检测上下文压缩中答案之外的静默证据丢失

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