LinguDistill: Recovering Linguistic Ability in Vision- Language Models via Selective Cross-Modal Distillation

Patrick Amadeus Irawan, Edd, Shanu Kumar, Alham Fikri Aji, Yova Kementchedjhieva

LinguDistill: Recovering Linguistic Ability in Vision- Language Models via Selective Cross-Modal Distillation: 4 upvotes on Hugging Face Daily Papers, #31 of 45 papers on 2026-04-03. Day-by-day upvote history.

Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representation shift and cross-modal interference introduced during multimodal adaptation. Such loss is difficult to recover, even with targeted task-specific fine-tuning using standard objectives. Prior recovery approaches typically introduce additional modules that act as intermediate alignment layers to maintain or isolate modality-specific subspaces, which increases architectural complexity, adds parameters at inference time, and limits flexibility across models and settings. We propose LinguDistill, an adapter-free distillation method that restores linguistic capability by utilizing the original frozen LM as a teacher. We overcome the key challenge of enabling vision-conditioned teacher supervision by introducing layer-wise KV-cache sharing, which exposes the teacher to the student's multimodal representations without modifying the architecture of either model. We then selectively distill the teacher's strong linguistic signal on language-intensive data to recover language capability, while preserving the student's visual grounding on multimodal tasks. As a result, LinguDistill recovers sim10% of the performance lost on language and knowledge benchmarks, while maintaining comparable performance on vision-heavy tasks. Our findings demonstrate that linguistic capability can be recovered without additional modules, providing an efficient and practical solution to modality-specific degradation in multimodal models.

Paper page on Hugging Face · arXiv

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