Breaking Language Barriers in Visual Language Models via Multilingual Textual Regularization

ACL ARR 2025 July Submission201 Authors

Abstract: Rapid advancements in Visual Language Models (VLMs) have transformed multimodal understanding but are often constrained by generating English responses regardless of the input language. This phenomenon has been termed as Image-induced Fidelity Loss (IFL) and stems from limited multimodal multilingual training data. To address this, we propose a continuous multilingual integration strategy that injects text-only multilingual data during visual instruction tuning, preserving the language model's original multilingual capabilities. Extensive evaluations demonstrate that our approach significantly improves linguistic fidelity across languages without degradation in visual performance. We also explore model merging, which improves language fidelity but comes at the cost of visual performance. In contrast, our core method achieves robust multilingual alignment without trade-offs, offering a scalable and effective path to mitigating IFL for global VLM adoption.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: multimodality,vision question answering

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings

Languages Studied: German,Dutch, Spanish,English,French,Russian

Previous URL: https://openreview.net/forum?id=pQniDikzNO

Reassignment Request Area Chair: No, I want the same area chair from our previous submission (subject to their availability).

Reassignment Request Reviewers: No, I want the same set of reviewers from our previous submission (subject to their availability)

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: Yes

A2 Elaboration: Named as Ethical Considerations, discussed after Limitations section.

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: 3

B2 Discuss The License For Artifacts: No

B2 Elaboration: We have added the sources of the data and models we have used, and each source indicates the license of usage.

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: 3

B4 Data Contains Personally Identifying Info Or Offensive Content: N/A

B5 Documentation Of Artifacts: Yes

B5 Elaboration: 3

B6 Statistics For Data: Yes

B6 Elaboration: 3

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: 3

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Appendix E

C3 Descriptive Statistics: Yes

C3 Elaboration: 4

C4 Parameters For Packages: Yes

C4 Elaboration: 4

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: No

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 201