Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering 文章

arXiv:2605.24297v1 Announce Type: cross Abstract: Which fine-tuning signals improve patent embedding models, and do gains transfer across patent landscapes? We benchmark 22 embedding models, from 22M-parameter encoders to 12B instruction-tuned LLMs, on retrieval, classification, and clustering. The study uses 113,148 WIPO assistive-technology patents, 46,069 citation-graph retrieval queries, and the public DAPFAM dataset for external validation. Our framework covers citation-based retrieval, hybrid sparse-dense fusion, multi-label classification over five datasets, unsupervised clustering, six text-section views, domain-adaptive fine-tuning of four models, jurisdiction analysis, and proprietary DWPI (Derwent World Patents Index, Clarivate) expert-written content. Results show that fine-tuning is task-dependent: single-landscape tuning can improve in-domain scores but often hurts retrieval on an external landscape, challenging the assumption that more domain data always helps.