AI RESEARCH
Reflect then Learn: Active Prompting for Information Extraction Guided by Introspective Confusion
arXiv CS.AI
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ArXi:2508.10036v2 Announce Type: replace-cross Large Language Models (LLMs) show remarkable potential for few-shot information extraction (IE), yet their performance is highly sensitive to the choice of in-context examples. Conventional selection strategies often fail to provide informative guidance, as they overlook a key source of model fallibility: confusion stemming not just from semantic content, but also from the generation of well-structured formats required by IE tasks. To address this, we.