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A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Multilingual language models often produce inconsistent answers to semantically equivalent questions across languages, motivating methods to improve cross-lingual consistency (CLC). However, existing methods are typically evaluated using different models, tasks, and protocols, leaving their relative strengths unclear. In this work, we present a unified evaluation of representative CLC-enhancement methods for question answering, spanning inference-time interventions and post-training approaches across three model families and three closed-form benchmarks. The results show that post-training met

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