Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion

• URL: http://arxiv.org/abs/2604.22261v1
• Date: Fri, 24 Apr 2026 06:10:48 GMT
• Title: Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion
• Abstract summary: Large language models (LLMs) struggle with relation completion (RC)<n>We propose a novel multi-stage paraphrase-guided relation-completion framework, RC-RAG.<n>Experiments with five LLMs on two benchmark datasets show that RC-RAG consistently outperforms several RAG baselines.
• Score: 23.973722690980065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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