Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples

• URL: http://arxiv.org/abs/2502.08638v4
• Date: Thu, 09 Oct 2025 14:39:40 GMT
• Title: Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
• Authors: Andrianos Michail, Simon Clematide, Rico Sennrich,
• Abstract summary: We introduce Cross-Lingual Semantic Discrimination (D), a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors.<n> CLSD measures an embedding model's ability to rank the true parallel sentence above semantically misleading but lexically similar alternatives.<n>Our experiments show that models fine-tuned for retrieval tasks benefit from pivoting through English, whereas bitext mining models perform best in direct cross-lingual settings.
• Score: 29.62231663945077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evaluation of cross-lingual semantic search models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. We introduce Cross-Lingual Semantic Discrimination (CLSD), a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. CLSD measures an embedding model's ability to rank the true parallel sentence above semantically misleading but lexically similar alternatives. As a case study, we construct CLSD datasets for German--French in the news domain. Our experiments show that models fine-tuned for retrieval tasks benefit from pivoting through English, whereas bitext mining models perform best in direct cross-lingual settings. A fine-grained similarity analysis further reveals that embedding models differ in their sensitivity to linguistic perturbations. We release our code and datasets under AGPL-3.0: https://github.com/impresso/cross_lingual_semantic_discrimination

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