通过多阶段改写增强，提升大模型在稀有关系上的补全能力。
Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion
- 分三阶段注入关系改写文本，扩大检索词汇覆盖
- 长尾场景下准确率提升40.6点，超越基线13.8点以上
- 无需微调，适合资源有限的部署场景
大语言模型在关系补全任务中，尤其在信息稀疏或罕见关系场景下表现不佳，无论是否采用检索增强生成（RAG）。为此，本文提出一种多阶段改写引导的关系补全框架RC-RAG，系统性地在多个阶段融入关系改写表达：(a) 在检索阶段引入改写以扩展关系词汇覆盖；(b) 利用改写生成关系感知摘要；(c) 在生成阶段使用改写引导推理。该方法无需任何模型微调。在两个基准数据集上，使用五种LLM进行实验表明，RC-RAG持续优于多个RAG基线。在长尾设置下，最佳模型性能相比独立运行提升40.6点精确匹配（EM），分别超过两个强基线16.0和13.8 EM点，且计算开销低。
原文摘要 · Abstract (English)
Large language models (LLMs) struggle with relation completion (RC), both with and without retrieval-augmented generation (RAG), particularly when the required information is rare or sparsely represented. To address this, we propose a novel multi-stage paraphrase-guided relation-completion framework, RC-RAG, that systematically incorporates relation paraphrases across multiple stages. In particular, RC-RAG: (a) integrates paraphrases into retrieval to expand lexical coverage of the relation, (b) uses paraphrases to generate relation-aware summaries, and (c) leverages paraphrases during generation to guide reasoning for relation completion. Importantly, our method does not require any model fine-tuning. Experiments with five LLMs on two benchmark datasets show that RC-RAG consistently outperforms several RAG baselines. In long-tail settings, the best-performing LLM augmented with RC-RAG improves by 40.6 Exact Match (EM) points over its standalone performance and surpasses two strong RAG baselines by 16.0 and 13.8 EM points, respectively, while maintaining low computational overhead.
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