mlsanityData to 14 Oct 2026

LLMs Can Compensate for Deficiencies in Visual Representations

arXiv 2506.05439 ↗First posted 5 Jun 2025cs.CV cs.AI cs.CLcited by 3

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Many vision-language models (VLMs) that prove very effective at a range of multimodal task, build on CLIP-based vision encoders, which are known to have various limitations. We investigate the hypothesis that the strong language backbone in VLMs compensates for possibly weak visual features by contextualizing or enriching them. Using three CLIP-based VLMs, we perform controlled self-attention ablations on a carefully designed probing task. Our findings show that despite known limitations, CLIP visual representations offer ready-to-read semantic information to the language decoder. However, in scenarios of reduced contextualization in the visual representations, the language decoder can largely compensate for the deficiency and recover performance. This suggests a dynamic division of labor in VLMs and motivates future architectures that offload more visual processing to the language decoder.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 28 of its 41 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

28 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution18 Sep
  2. 2024Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space26 Feb
  3. 2023Improved Baselines with Visual Instruction Tuning5 Oct

Other references · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  4. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  5. 2024TinyLlama: An Open-Source Small Language Modelcited by 618
  6. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 528
  7. 2021Image Segmentation Using Text and Image Promptscited by 512
  8. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 512
  9. 2024Evaluating Text-to-Visual Generation with Image-to-Text Generationcited by 474
  10. 2024Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Modelscited by 339
  11. 2023Interpreting CLIP's Image Representation via Text-Based Decompositioncited by 196
  12. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMscited by 179
13 more
  1. 2023SCLIP: Rethinking Self-Attention for Dense Vision-Language Inferencecited by 170
  2. 2024Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lenscited by 143
  3. 2024DOCCI: Descriptions of Connected and Contrasting Imagescited by 132
  4. 2022Does CLIP Bind Concepts? Probing Compositionality in Large Image Modelscited by 102
  5. 2021MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuningcited by 85
  6. 2022What do Vision Transformers Learn? A Visual Explorationcited by 76
  7. 2021Part-aware Panoptic Segmentationcited by 56
  8. 2024Interpreting the Second-Order Effects of Neurons in CLIPcited by 36
  9. 2023Analyzing Vision Transformers for Image Classification in Class Embedding Spacecited by 26
  10. 2023Investigating the Limitation of CLIP Models: The Worst-Performing Categoriescited by 12
  11. 2024Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encodercited by 11
  12. 2024Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIPcited by 9
  13. 2025Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Studycited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchorscited by 13
  2. 2026Pathways of Visual Information Flow in Vision-Language Modelscited by 5
  3. 2026Certified Interface Aliases: Exact Collisions in Vision-Language Preprocessing, and When They Existcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hidden in plain sight: VLMs overlook their visual representations
  2. 2023From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
  3. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
  4. 2025Visual Representations inside the Language Model
  5. 2025How Visual Representations Map to Language Feature Space in Multimodal LLMs
  6. 2026Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
  7. 2025VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions
  8. 2026Do Vision Language Models Need to Process Image Tokens?