QaECTER sets new state-of-the-art patent retrieval performance on the new Sophia-Bench benchmark and an external test, outperforming a 23x larger general model and all prior patent-specific models using citation-driven training.
PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding
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Multi-task evaluation of 22 patent embedding models finds task-specific fine-tuning benefits and significant cross-landscape retrieval degradation that cannot be fixed by hybrid fusion.
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Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench
QaECTER sets new state-of-the-art patent retrieval performance on the new Sophia-Bench benchmark and an external test, outperforming a 23x larger general model and all prior patent-specific models using citation-driven training.
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Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering
Multi-task evaluation of 22 patent embedding models finds task-specific fine-tuning benefits and significant cross-landscape retrieval degradation that cannot be fixed by hybrid fusion.