From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification

Paper recorded by Signals 4 on 2026-09-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02

Category: cs.AI · 人工智能 · first seen 2026-09-02

Abstract

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 does

Read on arXiv →