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

• URL: http://arxiv.org/abs/2502.08638v3
• Date: Thu, 20 Feb 2025 10:51:46 GMT
• Title: Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
• Authors: Andrianos Michail, Simon Clematide, Rico Sennrich,
• Abstract summary: This paper introduces a novel cross-lingual search task that does not require a large semantic corpus. It focuses on the ability of a model to cross-lingually rank the true parallel sentence higher than challenging distractors generated by a large language model. We create a case study of our introduced CLSD task for the language pair German-French in the news domain.
• Score: 38.18495961129682
• License:
• Abstract: The evaluation of cross-lingual semantic search capabilities of models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. To allow for domain-specific evaluation, we introduce Cross Lingual Semantic Discrimination (CLSD), a novel cross-lingual semantic search task that does not require a large evaluation corpus, only parallel sentences of the language pair of interest within the target domain. This task focuses on the ability of a model to cross-lingually rank the true parallel sentence higher than challenging distractors generated by a large language model. We create a case study of our introduced CLSD task for the language pair German-French in the news domain. Within this case study, we find that models that are also fine-tuned for retrieval tasks benefit from pivoting through English, while bitext mining models perform best directly cross-lingually. A fine-grained similarity analysis enabled by our distractor generation strategy indicate that different embedding models are sensitive to different types of perturbations.

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