# DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval

arXiv: https://arxiv.org/abs/2506.22141 · HTML: https://mlsanity.com/paper/2506.22141/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [Deeper Text Understanding for IR with Contextual Neural Language Modeling](https://mlsanity.com/paper/1905.09217/) (2019)
- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)

## Built on by (most cited first, 5 of 5)

- [PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding](https://mlsanity.com/paper/2510.22264/) (2025)
- [Formally Verified Patent Analysis via Dependent Type Theory: Machine-Checkable Certificates from a Hybrid AI + Lean 4 Pipeline](https://mlsanity.com/paper/2604.18882/) (2026)
- [Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench](https://mlsanity.com/paper/2604.22897/) (2026)
- [HAKARI-Bench: A Lightweight Benchmark for Comparing Retrieval Architectures and Efficiency Settings under Unified Conditions](https://mlsanity.com/paper/2606.22778/) (2026)
- [REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling](https://mlsanity.com/paper/2608.29899/) (2026)

## Abstract (arXiv)

Patent prior-art retrieval becomes especially challenging when relevant disclosures cross technological boundaries. Existing benchmarks lack explicit domain partitions, making it difficult to assess how retrieval systems cope with such shifts. We introduce DAPFAM, a family-level benchmark with explicit IN-domain and OUT-domain partitions defined by a new IPC3 overlap scheme. The dataset contains 1,247 query families and 45,336 target families aggregated at the family level to reduce international redundancy, with citation based relevance judgments. We conduct 249 controlled experiments spanning lexical (BM25) and dense (transformer) backends, document and passage level retrieval, multiple query and document representations, aggregation strategies, and hybrid fusion via Reciprocal Rank Fusion (RRF). Results reveal a pronounced domain gap: OUT-domain performance remains roughly five times lower than IN-domain across all configurations. Passage-level retrieval consistently outperforms document-level, and dense methods provide modest gains over BM25, but none close the OUT-domain gap. Document-level RRF yields strong effectiveness efficiency trade-offs with minimal overhead. By exposing the persistent challenge of cross-domain retrieval, DAPFAM provides a reproducible, compute-aware testbed for developing more robust patent IR systems. The dataset is publicly available on huggingface at https://huggingface.co/datasets/datalyes/DAPFAM_patent.