# Where do Large Vision-Language Models Look at when Answering Questions?

arXiv: https://arxiv.org/abs/2503.13891 · HTML: https://mlsanity.com/paper/2503.13891/

## Key facts

- Cited by 15 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 41 of its 54 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 41)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Are We on the Right Way for Evaluating Large Vision-Language Models?](https://mlsanity.com/paper/2403.20330/) (2024)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://mlsanity.com/paper/2403.06764/) (2024)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models](https://mlsanity.com/paper/2403.18814/) (2024)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers](https://mlsanity.com/paper/2103.15679/) (2021)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)

## Built on by (most cited first, 15 of 15)

- [Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation](https://mlsanity.com/paper/2509.22496/) (2025)
- [V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models](https://mlsanity.com/paper/2509.14837/) (2025)
- [LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents](https://mlsanity.com/paper/2507.10610/) (2025)
- [Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations](https://mlsanity.com/paper/2505.17812/) (2025)
- [SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling](https://mlsanity.com/paper/2508.09105/) (2025)
- [Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain](https://mlsanity.com/paper/2602.17186/) (2026)
- [From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents](https://mlsanity.com/paper/2604.19775/) (2026)
- [Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints](https://mlsanity.com/paper/2602.07008/) (2026)
- [TraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding](https://mlsanity.com/paper/2602.19768/) (2026)
- [Towards Accurate UAV Image Perception: Guiding Vision-Language Models with Stronger Task Prompts](https://mlsanity.com/paper/2512.07302/) (2025)
- [Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability](https://mlsanity.com/paper/2605.22168/) (2026)
- [PhaseWin: An Efficient Search Algorithm for Faithful Visual Attribution](https://mlsanity.com/paper/2606.18008/) (2026)
- [A Good Initialization is All You Need for Faithful Visual Attribution](https://mlsanity.com/paper/2607.06726/) (2026)
- [Clustering and Token Denoising for Faster and More Robust VLMs](https://mlsanity.com/paper/2608.19285/) (2026)
- [AnswerMap: Faithful Spatial Interpretability of VLMs from Answer Posteriors](https://mlsanity.com/paper/2609.35247/) (2026)

## Abstract (arXiv)

Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain underexplored. A fundamental question arises: to what extent do LVLMs rely on visual input, and which image regions contribute to their responses? It is non-trivial to interpret the free-form generation of LVLMs due to their complicated visual architecture (e.g., multiple encoders and multi-resolution) and variable-length outputs. In this paper, we extend existing heatmap visualization methods (e.g., iGOS++) to support LVLMs for open-ended visual question answering. We propose a method to select visually relevant tokens that reflect the relevance between generated answers and input image. Furthermore, we conduct a comprehensive analysis of state-of-the-art LVLMs on benchmarks designed to require visual information to answer. Our findings offer several insights into LVLM behavior, including the relationship between focus region and answer correctness, differences in visual attention across architectures, and the impact of LLM scale on visual understanding. The code and data are available at https://github.com/bytedance/LVLM_Interpretation.