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

**Authors:** Gabriela Ben Melech Stan, Raanan Yehezkel Rohekar, Yaniv Gurwicz, Matthew Lyle Olson, Anahita Bhiwandiwalla, Estelle Aflalo, Chenfei Wu, Nan Duan, Shao-Yen Tseng, Vasudev Lal  
**arXiv:** [2404.03118](https://arxiv.org/abs/2404.03118)  
**Date:** 2024-04-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.03118v1/

## Key points

- Introduction of LVLM-Interpret, an interactive tool for interpreting responses from large vision-language models (LVLMs)
- Features of the tool: visualization of output related to input images through raw attention, relevancy maps, and causal interpretation
- Ability for users to explore inner mechanisms of LVLMs and gain insights on failure cases using various interpretability functions
- Future work suggestion to consolidate multiple methods for a more comprehensive metric explaining model responses
- Challenges posed by hallucination in LVLMs despite their remarkable progress in tasks like summarization, translation, question answering, and creative writing

## Summary

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In this paper, the authors introduce LVLM-Interpret, an interactive tool designed to interpret responses from large vision-language models (LVLMs). The tool offers a visualization of how generated outputs are related to input images through raw attention, relevancy maps, and causal interpretation. Users can explore the inner mechanisms of LVLMs and gain insights on failure cases using various interpretability functions provided by the tool. The authors suggest future work could involve consolidating multiple methods for a more comprehensive metric to explain the reasoning behind model responses. LVLMs have shown remarkable progress in tasks such as summarization, translation, general question answering, and creative writing, often surpassing human capabilities. However, these models are still susceptible to hallucination – generating untrue information. This phenomenon is also observed in LVLMs and may even extend into additional dimensions stemming from the visual modality. With the introduction of LVLMs with a high number of parameters, interpreting and explaining model outputs to mitigate hallucination poses a significant challenge. The need to understand the reasoning behind model responses led to the development of LVLM-Interpret as an interpretability tool for large vision-language models. The main contributions of this application include adaptations of multiple interpretability methods tailored for interactive analysis of LVLMs. These methods encompass raw attention, relevancy maps, and causal interpretation and are applicable to any LVLM with a transformer-based LLM front-end. The authors demonstrate a case study showcasing how LVLM-Interpret can be utilized to enhance understanding of the internal workings and failure mechanisms in LLaVA. Previous works have laid the foundation for novel interpretability tools in deep learning models, utilizing explanatory graphs, decision trees, histograms among others. As Transformer-based architectures gained popularity in the field, approaches like computing relevancy scores across layers or generalizing attention from low-level features to high-level concepts have emerged. The paper's main authors include Gabriela Ben Melech Stan*, Estelle Aflalo*, Raanan Yehezkel Rohekar*, Anahita Bhiwandiwalla*, Shao-Yen Tseng*, Matthew Lyle Olson*, Yaniv Gurwicz*, Chenfei Wu**, Nan Duan**, Vasudev Lal*. The affiliations include Intel Labs and Microsoft Research Asia.