[Submitted on 3 Sep 2026]
Title:A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models
View a PDF of the paper titled A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models, by Jirui Qi and 4 other authors
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Abstract: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 methods are generally more reliable, with direct distribution alignment consistently improving CLC across all model-dataset combinations, while other methods are more sensitive to answer format and the breadth of language coverage. Notably, cross-domain transfer is limited unless source and target tasks share similar output formats. We further investigate whether CLC enhancement hurts models' ability to respond differently *when needed*, that is, when asked culture-dependent questions. Across two benchmarks of culturally diverse question answering, we find no systematic degradation in controlled closed-form evaluation, whereas open-ended generation reveals occasional accuracy reductions, particularly for non-English responses. Our work highlights the need to evaluate CLC enhancement for both cross-domain robustness and culturally appropriate variation, informing future work in post-training and benchmark development.
Comments: Preprint. All code and datasets will be released upon publication
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.04409 [cs.CL]
(or arXiv:2609.04409v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.04409
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jirui Qi [view email] [v1] Thu, 3 Sep 2026 19:18:44 UTC (1,645 KB)
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