这篇论文解决了大模型分类相似标签的难题，无需微调就能提升效果，小模型也能受益。
研究人员提出了一种新框架，解决大模型在语义相似标签分类中的困难。该框架能识别模型难以区分的标签对，扩展候选集并生成针对性规则。在WOS、Flipkart和LEDGAR三个基准测试中，该方法比检索基线提升Macro F1最高达10.0个百分点。小模型(2B-20B参数)通过跨模型迁移可提升最高11.5个百分点。
From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification
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 not help the model tell similar ones apart. When two similar labels both appear as candidates, the model lacks the signal to choose correctly between them. We propose a framework that (1) identifies which label pairs the model struggles to distinguish, (2) expands the candidate set to include confusable labels, and (3) generates targeted rules to differentiate between similar candidates. The framework requires no fine-tuning, and the generated rules transfer to smaller, cheaper models. On three benchmarks (WOS, Flipkart, LEDGAR), our approach improves Macro F1 by up to 10.0pp over retrieval baselines, with smaller models (2B--20B) gaining up to 11.5pp via cross-model transfer.