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Billiger.de Products: A Bilingual Entity Matching Benchmark

arXiv 2609.37713 ↗First posted 29 Sep 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 29 Sep 2026.

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References · 18

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2022Can Foundation Models Wrangle Your Data?cited by 173
  4. 2020Deep Entity Matching with Pre-Trained Language Modelscited by 143
  5. 2020Neural Networks for Entity Matching: A Surveycited by 33
  6. 2024Match, Compare, or Select? An Investigation of Large Language Models for Entity Matchingcited by 32
  7. 2023Entity Matching using Large Language Modelscited by 31
  8. 2022Supervised Contrastive Learning for Product Matchingcited by 24
  9. 2020(Almost) All of Entity Resolutioncited by 23
  10. 2023Using ChatGPT for Entity Matchingcited by 22
  11. 2023WDC Products: A Multi-Dimensional Entity Matching Benchmarkcited by 17
  12. 2021Machamp: A Generalized Entity Matching Benchmarkcited by 16
6 more
  1. 2024Fine-tuning Large Language Models for Entity Matchingcited by 16
  2. 2022Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Constructioncited by 10
  3. 2021Alaska: A Flexible Benchmark for Data Integration Taskscited by 7
  4. 2021Cross-Language Learning for Entity Matchingcited by 3
  5. 2023ProMap: Datasets for Product Mapping in E-commercecited by 2
  6. 2022Multilingual Transformers for Product Matching -- Experiments and a New Benchmark in Polishcited by 1

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