# GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks

**Authors:** Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, Linda Ruth Petzold  
**arXiv:** [2311.01361](https://arxiv.org/abs/2311.01361)  
**Date:** 2023-11-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.01361v1/

## Key points

- Authors address the challenge of automatically evaluating vision-language tasks
- GPT-4V has shown promising results in multi-modal tasks, but its potential as a generalist evaluator has not been explored
- Authors conduct experiments on a wide range of tasks including image-to-text synthesis, text-to-image synthesis, image-to-image translations, and multi images to text alignment
- Evaluation methods used are single answer grading and pairwise comparison using GPT-4V
- Results show promising agreement with humans across different tasks and evaluation methods
- GPT-4V has limitations such as restricted visual clarity grading and real-world complex reasoning
- GPT-4V's ability to provide human-aligned scores with detailed explanations is promising for developing a universal automatic evaluator
- Study expands understanding of leveraging GPT-4V as an evaluator for vision language tasks

## Summary

In the paper titled "GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks," authors Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, and Linda Ruth Petzold address the challenge of automatically evaluating vision-language tasks. They highlight that reflecting human judgments accurately is difficult due to limitations in accounting for fine-grained details. While GPT-4V has shown promising results in various multi-modal tasks, its potential as a generalist evaluator for these tasks has not been systematically explored. To comprehensively validate GPT-4V's capabilities for evaluation purposes, the authors conduct experiments on a wide range of tasks. These tasks include image-to-text and text-to-image synthesis at foundational levels as well as high-level image-to-image translations and multi images to text alignment. The evaluation methods employed by the authors are single answer grading and pairwise comparison using GPT-4V. The results demonstrate that GPT 4V exhibits promising agreement with humans across different tasks and evaluation methods. This finding highlights the immense potential of multi modal Language Models (LLMs) like GPT 4V as evaluators. Despite some limitations such as restricted visual clarity grading and real world complex reasoning, GPT 4V's ability to provide human aligned scores enriched with detailed explanations is promising for developing a universal automatic evaluator. Overall, this study expands our understanding of leveraging GPT 4V as an evaluator for vision language tasks. The findings suggest that GPT 4V can effectively evaluate various multi modal tasks and provide valuable insights into their performance.