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News/From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence
arxiv
PublishedOctober 15, 2026 at 4:00 AM

From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence

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arXiv:2610.20417v1 Announce Type: new Abstract: Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of, and whether confidence signals can turn unreliable output into usable output. We

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arxivRCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods5harxivCan Graph Learning Learn Circuits?5harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery5harxivFrom Events to Reasoning: Hierarchical Rule Composition over Explicit States5h
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