VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917685816721408 |
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| author | Koh, Jing Yu Lo, Robert Jang, Lawrence Duvvur, Vikram Lim, Ming Chong Huang, Po-Yu Neubig, Graham Zhou, Shuyan Salakhutdinov, Ruslan Fried, Daniel |
| author_facet | Koh, Jing Yu Lo, Robert Jang, Lawrence Duvvur, Vikram Lim, Ming Chong Huang, Po-Yu Neubig, Graham Zhou, Shuyan Salakhutdinov, Ruslan Fried, Daniel |
| contents | Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on realistic \textit{visually grounded tasks}. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web. Our code, baseline models, and data is publicly available at https://jykoh.com/vwa. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks Koh, Jing Yu Lo, Robert Jang, Lawrence Duvvur, Vikram Lim, Ming Chong Huang, Po-Yu Neubig, Graham Zhou, Shuyan Salakhutdinov, Ruslan Fried, Daniel Machine Learning Computation and Language Computer Vision and Pattern Recognition Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on realistic \textit{visually grounded tasks}. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web. Our code, baseline models, and data is publicly available at https://jykoh.com/vwa. |
| title | VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks |
| topic | Machine Learning Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.13649 |