# LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models

arXiv: https://arxiv.org/abs/2404.03118 · HTML: https://mlsanity.com/paper/2404.03118/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)
- Mechanistic interpretability / [Interpreting vision and multimodal models](https://mlsanity.com/mechinterp/interpreting-vision-and-multimodal-models/)

## Builds on (most cited first, 11 of 11)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Evaluation and Analysis of Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2308.15126/) (2023)
- [ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition](https://mlsanity.com/paper/2208.10431/) (2022)
- [Interpretability-Aware Vision Transformer](https://mlsanity.com/paper/2309.08035/) (2023)
- [CLEAR: Causal Explanations from Attention in Neural Recommenders](https://mlsanity.com/paper/2210.10621/) (2022)
- [ComFe: An Interpretable Head for Vision Transformers](https://mlsanity.com/paper/2403.04125/) (2024)

## Built on by (most cited first, 24 of 24)

- [See What You Are Told: Visual Attention Sink in Large Multimodal Models](https://mlsanity.com/paper/2503.03321/) (2025)
- [DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models](https://mlsanity.com/paper/2405.20985/) (2024)
- [ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models](https://mlsanity.com/paper/2407.21534/) (2024)
- [Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy](https://mlsanity.com/paper/2412.17759/) (2024)
- [MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs](https://mlsanity.com/paper/2511.14159/) (2025)
- [Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation](https://mlsanity.com/paper/2506.07214/) (2025)
- [Probing the Representational Power of Sparse Autoencoders in Vision Models](https://mlsanity.com/paper/2508.11277/) (2025)
- [V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models](https://mlsanity.com/paper/2509.14837/) (2025)
- [How Vision Becomes Language: A Layer-wise Information-Theoretic Analysis of Multimodal Reasoning](https://mlsanity.com/paper/2602.15580/) (2026)
- [SurgXBench: Explainable Vision-Language Model Benchmark for Surgery](https://mlsanity.com/paper/2505.10764/) (2025)
- [GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models](https://mlsanity.com/paper/2506.18985/) (2025)
- [Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models](https://mlsanity.com/paper/2509.22415/) (2025)
- [Can VLMs Detect and Localize Fine-Grained AI-Edited Images?](https://mlsanity.com/paper/2505.15644/) (2025)
- [FastRM: An efficient and automatic explainability framework for multimodal generative models](https://mlsanity.com/paper/2412.01487/) (2024)
- [On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AI](https://mlsanity.com/paper/2508.00171/) (2025)
- [VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models](https://mlsanity.com/paper/2410.04609/) (2024)
- [Augmented Vision-Language Models: A Systematic Review](https://mlsanity.com/paper/2507.22933/) (2025)
- [Towards Understanding Multimodal Fine-Tuning: Spatial Features](https://mlsanity.com/paper/2602.08713/) (2026)
- [Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability](https://mlsanity.com/paper/2406.12897/) (2024)
- [Adapting Lightweight Vision Language Models for Radiological Visual Question Answering](https://mlsanity.com/paper/2506.14451/) (2025)
- [Structural Graph Probing of Vision-Language Models](https://mlsanity.com/paper/2603.27070/) (2026)
- [Real-Time Visual Attribution Streaming in Thinking Model](https://mlsanity.com/paper/2604.16587/) (2026)
- [Multimodal Model Diffing for Feature Discovery and Control](https://mlsanity.com/paper/2608.09928/) (2026)
- [When, Where, and Why: Hierarchical Causal Explainability for Video Face Understanding with MLLMs](https://mlsanity.com/paper/2610.17791/) (2026)

## Abstract (arXiv)

In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various forms of data input, are becoming increasingly popular. However, understanding their internal mechanisms remains a complex task. Numerous advancements have been made in the field of explainability tools and mechanisms, yet there is still much to explore. In this work, we present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer, and assess the efficacy of the language model in grounding its output in the image. With our application, a user can systematically investigate the model and uncover system limitations, paving the way for enhancements in system capabilities. Finally, we present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.