Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference

• URL: http://arxiv.org/abs/2508.14735v1
• Date: Wed, 20 Aug 2025 14:30:34 GMT
• Title: Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference
• Authors: Samir Abdaljalil, Erchin Serpedin, Khalid Qaraqe, Hasan Kurban,
• Abstract summary: Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplored.<n>We present a framework for multilingual natural language inference that generates synthetic, logic-based premise-hypothesis pairs and translates them into a typologically diverse set of languages.<n>Code-switching does not degrade, and can even improve, performance, suggesting that translation-induced lexical variation may serve as a regularization signal.
• Score: 2.172419551358714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplored. We present a controlled evaluation framework for multilingual natural language inference (NLI) that generates synthetic, logic-based premise-hypothesis pairs and translates them into a typologically diverse set of languages. This design enables precise control over semantic relations and allows testing in both monolingual and mixed-language (code-switched) conditions. Surprisingly, code-switching does not degrade, and can even improve, performance, suggesting that translation-induced lexical variation may serve as a regularization signal. We validate semantic preservation through embedding-based similarity analyses and cross-lingual alignment visualizations, confirming the fidelity of translated pairs. Our findings expose both the potential and the brittleness of current LLM cross-lingual reasoning, and identify code-switching as a promising lever for improving multilingual robustness. Code available at: https://github.com/KurbanIntelligenceLab/nli-stress-testing

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