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This paper introduces Billiger.de Products, a new bilingual benchmark designed to evaluate entity matching models across thirteen diverse consumer categories in both German and English. The study highlights significant performance gaps between language versions for traditional matchers while demonstrating that zero-shot LLMs remain largely insensitive to the specific language used. For researchers focusing on data-centric AI and cross-lingual generalization, this dataset offers a rigorous testbed beyond the limitations of current English-dominated benchmarks.
Billiger.de Products: A Bilingual Entity Matching Benchmark
arXiv:2609.37713v1 Announce Type: new Abstract: Existing product matching benchmarks primarily contain English-language product data and are often dominated by a single product category, such as electronics. This paper introduces Billiger.de Products, a bilingual German and English entity matching benchmark covering thirteen consumer product categories, including difficult-to-handle categories such as clothing and furniture. The benchmark data originates from the German price comparison platform billiger.de. Following the design of WDC Products, the benchmark offers multiple variants that differ in the fraction of corner cases, the size of the development set, and the fraction of entities unseen during training. An aligned English translation of every offer keeps all pairs, splits, and labels fixed, while cross-language test sets combine German and English records within individual pairs. We validate the benchmark using six supervised matchers and zero-shot GPT-5.2 on both language versions and the cross-language test sets. The validation shows the difficulty of the benchmark. The comparison of the results on the English version of the benchmark to the results on the German version shows that most matchers score on average higher on the English version. The difference is largest for RoBERTa and HierGAT, while the zero-shot LLM runs are largely insensitive to the language. Comparing the F1 scores achieved by PLM-based matchers on the English version of Billiger.de Products with their performance on existing English-language benchmarks, such as WDC Products and Abt-Buy, shows that Billiger.de Products is more difficult than these benchmarks.
Title:Billiger.de Products: A Bilingual Entity Matching Benchmark
Abstract:Existing product matching benchmarks primarily contain English-language product data and are often dominated by a single product category, such as electronics. This paper introduces this http URL Products, a bilingual German and English entity matching benchmark covering thirteen consumer product categories, including difficult-to-handle categories such as clothing and furniture. The benchmark data originates from the German price comparison platform this http URL. Following the design of WDC Products, the benchmark offers multiple variants that differ in the fraction of corner cases, the size of the development set, and the fraction of entities unseen during training. An aligned English translation of every offer keeps all pairs, splits, and labels fixed, while cross-language test sets combine German and English records within individual pairs. We validate the benchmark using six supervised matchers and zero-shot GPT-5.2 on both language versions and the cross-language test sets. The validation shows the difficulty of the benchmark. The comparison of the results on the English version of the benchmark to the results on the German version shows that most matchers score on average higher on the English version. The difference is largest for RoBERTa and HierGAT, while the zero-shot LLM runs are largely insensitive to the language. Comparing the F1 scores achieved by PLM-based matchers on the English version of this http URL Products with their performance on existing English-language benchmarks, such as WDC Products and Abt-Buy, shows that this http URL Products is more difficult than these benchmarks.
23 pages. Data and code: this https URL
Computation and Language (cs.CL)
arXiv:2609.37713 [cs.CL]
(or arXiv:2609.37713v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.37713 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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