Summary of Visualwebarena: Evaluating Multimodal Agents on Realistic Visual Web Tasks, by Jing Yu Koh et al.
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
by Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary VisualWebArena is a benchmark designed to assess the performance of multimodal web agents on realistic, visually grounded tasks. The benchmark evaluates the ability of autonomous agents to process image-text inputs, interpret natural language instructions, and execute actions on websites. The goal is to automate computer tasks and provide a framework for evaluating multimodal language agents. We conducted an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Our results reveal limitations of text-only LLM agents and gaps in the capabilities of state-of-the-art multimodal language agents. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary VisualWebArena is a new way to test how well computers can understand pictures and words together on the internet. Right now, most computer tests only look at words, but we know that pictures are important too. To solve many problems online, you need both words and pictures. We created VisualWebArena to see how good computers are at using words and pictures together to get things done. |