SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents

• URL: http://arxiv.org/abs/2512.07538v1
• Date: Mon, 08 Dec 2025 13:17:27 GMT
• Title: SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents
• Authors: Michelle Wastl, Jannis Vamvas, Rico Sennrich,
• Abstract summary: SwissGov-RSD is the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition.<n>It encompasses a total of 224 multi-parallel documents in English-German, English-French, and English-Italian.<n>We evaluate a variety of open-source and closed source large language models as well as encoder models across different fine-tuning settings on this new benchmark.
• Score: 38.797311337915175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognizing semantic differences across documents, especially in different languages, is crucial for text generation evaluation and multilingual content alignment. However, as a standalone task it has received little attention. We address this by introducing SwissGov-RSD, the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition. It encompasses a total of 224 multi-parallel documents in English-German, English-French, and English-Italian with token-level difference annotations by human annotators. We evaluate a variety of open-source and closed source large language models as well as encoder models across different fine-tuning settings on this new benchmark. Our results show that current automatic approaches perform poorly compared to their performance on monolingual, sentence-level, and synthetic benchmarks, revealing a considerable gap for both LLMs and encoder models. We make our code and datasets publicly available.

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