VisualWebBench
A multimodal benchmark designed to assess the capabilities of multimodal large language models (MLLMs) across web page understanding and grounding tasks. Comprises 7 tasks (captioning, webpage QA, heading OCR, element OCR, element grounding, action prediction, and action grounding) with 1.5K human-curated instances from 139 real websites across 87 sub-domains.
Nova Pro from Amazon currently leads the VisualWebBench leaderboard with a score of 0.797 across 2 evaluated AI models.
What VisualWebBench measures
VisualWebBench is a multimodal benchmark that evaluates large language models on multimodal, frontend development, and vision tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for multimodal, best AI for frontend development and best AI for vision leaderboards.
Publication
- Paper
- VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
- Authors
- Junpeng Liu, Yifan Song, Bill Yuchen Lin, Wai Lam, and 3 others
- Published
- arXiv
- 2404.05955
Abstract
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. Existing benchmarks are either designed for general multimodal tasks, failing to capture the unique characteristics of web pages, or focus on end-to-end web agent tasks, unable to measure fine-grained abilities such as OCR, understanding, and grounding. In this paper, we introduce \bench{}, a multimodal benchmark designed to assess the capabilities of MLLMs across a variety of web tasks. \bench{} consists of seven tasks, and comprises 1.5K human-curated instances from 139 real websites, covering 87 sub-domains. We evaluate 14 open-source MLLMs, Gemini Pro, Claude-3 series, and GPT-4V(ision) on \bench{}, revealing significant challenges and performance gaps. Further analysis highlights the limitations of current MLLMs, including inadequate grounding in text-rich environments and subpar performance with low-resolution image inputs. We believe \bench{} will serve as a valuable resource for the research community and contribute to the creation of more powerful and versatile MLLMs for web-related applications.
Progress Over Time
Interactive timeline showing model performance evolution on VisualWebBench
VisualWebBench Leaderboard
FAQ
Common questions about VisualWebBench.
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