Retrieval-augmented generation systems for intellectual property

Retrieval-augmented generation systems are increasingly applied in the intellectual property industry, but they often fail to handle the diversity of real user queries. Spelling errors, colloquial expressions, and ambiguous terminology frequently cause retrievers to misinterpret semantically equivalent queries as unrelated, resulting in inaccurate responses. To address this problem, we propose the Multi-Angle Question Generation and Retrieval Fine-Tuning Method (MQG-RFM), a lightweight framework that leverages large language models to generate diverse paraphrased queries and uses hard negative examples to fine-tune retrievers. Rather than altering model architectures, MQG-RFM combines prompt engineering and fine-tuning to improve system robustness with low computational cost. Experiments on multiple intellectual property datasets demonstrate substantial gains in both retrieval accuracy and response quality compared with baselines. MQG-RFM offers a practical and scalable approach for small and medium-sized agencies. The proposed method has been successfully adopted by ScholarMate, the largest professional research networking platform in China, for real-world deployment.