{
  "id": 5001694,
  "title": "From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification",
  "url": "https://urgent.news/2026/09/01/from-confusion-to-clarity-confusion-aware-retrieval-and-knowledge",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-01T17:31:02.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.01564v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large language models (LLMs) struggle to classify text into taxonomies with many semantically similar labels, as the distinctions are domain-specific and not captured by pre-training. To handle large label spaces, a common approach retrieves top-$K$ candidate labels by embedding similarity and prompt the LLM to choose among them. However, top-$K$ retrieval reduces the number of candidates but…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}