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LLMs Distill Conceptual Knowledge to Vision Models.

Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee· June 29, 2026 View original

Summary

Researchers propose LaViD, a framework that transfers high-level semantic knowledge from a language-only LLM to a vision-only student model without paired multimodal data. LaViD uses LLM-generated multiple-choice questions to create conceptual signatures, outperforming methods that use vision-language models for distillation.

Large Language Models (LLMs) possess a vast repository of conceptual knowledge derived from extensive text pretraining, yet their potential to guide models in other modalities, particularly vision, remains largely untapped. This research introduces LaViD (Language-to-Visual Knowledge Distillation), a straightforward and effective framework designed to transfer sophisticated semantic knowledge from a language-only LLM teacher to a vision-only student model. Crucially, LaViD achieves this without requiring paired multimodal data, which simplifies the knowledge transfer process. LaViD operates by prompting an LLM to generate multiple-choice questions (MCQs) that specifically probe the semantic distinctions between various visual classes. Each visual class is then mapped to a soft label distribution over these MCQs, creating a rich "conceptual signature." This signature subsequently guides the vision student model through an auxiliary distillation loss. Remarkably, LaViD consistently surpasses recent methods that rely on vision-language models for distillation and achieves competitive or superior performance compared to state-of-the-art visual distillation techniques, with further improvements when combined with logit standardization. The framework also significantly enhances worst-group accuracy on datasets like Waterbirds, indicating improved robustness against spurious correlations.

Why it matters

This research offers a novel and efficient way to leverage the vast knowledge of LLMs to improve vision models, especially in fine-grained classification and robustness, without the costly need for paired multimodal datasets.

How to implement this in your domain

  1. 1Explore using language-only LLMs as teachers for vision models to transfer conceptual knowledge, reducing reliance on expensive paired multimodal data.
  2. 2Implement knowledge distillation techniques, specifically LaViD, to enhance the fine-grained classification capabilities and robustness of vision models.
  3. 3Investigate generating synthetic conceptual signals (e.g., MCQs) from LLMs to enrich training data for vision tasks.
  4. 4Apply this cross-modality transfer approach to improve model performance in domains where fine-grained visual distinctions are critical.

Who benefits

Computer VisionAI ResearchManufacturing (quality control)Healthcare (medical imaging)Retail (product recognition)

Key takeaways

  • LLMs can effectively transfer fine-grained conceptual knowledge to vision models.
  • LaViD framework uses LLM-generated MCQs for cross-modality knowledge distillation.
  • This method works without requiring paired multimodal data.
  • LaViD improves both classification performance and robustness against spurious correlations.

Original post by Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee

"arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models in other modalities remains underexplored. In this work, we propose LaViD--Lang…"

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Primary sources

Originally posted by Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee on X · view source

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