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

arXiv: https://arxiv.org/abs/2512.07538 · HTML: https://mlsanity.com/paper/2512.07538/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Scaling Unsupervised Word Alignment to Documents via Structural Constraints](https://mlsanity.com/paper/2608.21023/) (2026)
- [Swiss4Arg: A Parallel Dataset for Multilingual Argument Mining across Swiss National Languages](https://mlsanity.com/paper/2610.17894/) (2026)

## Abstract (arXiv)

Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in cross-lingual settings. 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 dataset publicly available.