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

Andrianos Michail; Simon Clematide; Rico Sennrich

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arXiv · 2502.08638

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

Andrianos Michail·Simon Clematide·Rico Sennrich

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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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Andrianos Michail, Simon Clematide, Rico Sennrich. 2025-02-12. Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples. https://arxiv.org/abs/2502.08638

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