Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs

• URL: http://arxiv.org/abs/2406.20098v1
• Date: Fri, 28 Jun 2024 17:59:46 GMT
• Title: Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
• Authors: Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, Eric P. Xing, Xiaodan Liang, Zhiqiang Shen,
• Abstract summary: Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio. Current MLLMs are surprisingly poor at understanding webpage screenshots and generating their corresponding HTML code. We propose Web2Code, a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning.
• Score: 112.89665642941814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However, current MLLMs are surprisingly poor at understanding webpage screenshots and generating their corresponding HTML code. To address this problem, we propose Web2Code, a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning and an evaluation framework for the webpage understanding and HTML code translation abilities of MLLMs. For dataset construction, we leverage pretrained LLMs to enhance existing webpage-to-code datasets as well as generate a diverse pool of new webpages rendered into images. Specifically, the inputs are webpage images and instructions, while the responses are the webpage's HTML code. We further include diverse natural language QA pairs about the webpage content in the responses to enable a more comprehensive understanding of the web content. To evaluate model performance in these tasks, we develop an evaluation framework for testing MLLMs' abilities in webpage understanding and web-to-code generation. Extensive experiments show that our proposed dataset is beneficial not only to our proposed tasks but also in the general visual domain, while previous datasets result in worse performance. We hope our work will contribute to the development of general MLLMs suitable for web-based content generation and task automation. Our data and code will be available at https://github.com/MBZUAI-LLM/web2code.

Related papers

• AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation [54.17246674188208]
  Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited adaptability and scalability when faced with a new website. We introduce the paradigm of generating web scrapers with large language models (LLMs) and propose AutoScraper, a two-stage framework that can handle diverse and changing web environments more efficiently.
  arXiv  Detail & Related papers  (2024-04-19T09:59:44Z)
• VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding? [115.60866817774641]
  Multimodal Large Language models (MLLMs) have shown promise in web-related tasks. evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. bench is a multimodal benchmark designed to assess the capabilities of MLLMs across a variety of web tasks.
  arXiv  Detail & Related papers  (2024-04-09T02:29:39Z)
• Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset [8.581656334758547]
  We introduce WebSight, a dataset consisting of 2 million pairs of HTML codes and their corresponding screenshots. To accelerate the research in this area, we open-source WebSight.
  arXiv  Detail & Related papers  (2024-03-14T01:40:40Z)
• Hierarchical Multimodal Pre-training for Visually Rich Webpage Understanding [22.00873805952277]
  WebLM is a multimodal pre-training network designed to address the limitations of solely modeling text and structure modalities of HTML in webpages. We propose several pre-training tasks to model the interaction among text, structure, and image modalities effectively. Empirical results demonstrate that the pre-trained WebLM significantly surpasses previous state-of-the-art pre-trained models across several webpage understanding tasks.
  arXiv  Detail & Related papers  (2024-02-28T11:50:36Z)
• Probing Multimodal Large Language Models for Global and Local Semantic Representations [57.25949445963422]
  We study which layers of Multimodal Large Language Models make the most effort to the global image information. In this study, we find that the intermediate layers of models can encode more global semantic information. We find that the topmost layers may excessively focus on local information, leading to a diminished ability to encode global information.
  arXiv  Detail & Related papers  (2024-02-27T08:27:15Z)
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis [69.15016747150868]
  We introduce WebAgent, an agent that learns from self-experience to complete tasks on real websites. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks.
  arXiv  Detail & Related papers  (2023-07-24T14:56:30Z)
• Understanding HTML with Large Language Models [73.92747433749271]
  Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. We contribute HTML understanding models (fine-tuned LLMs) and an in-depth analysis of their capabilities under three tasks. We show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks.
  arXiv  Detail & Related papers  (2022-10-08T07:27:17Z)
• DOM-LM: Learning Generalizable Representations for HTML Documents [33.742833774918786]
  We introduce a novel representation learning approach for web pages, dubbed DOM-LM, which addresses the limitations of existing approaches. We evaluate DOM-LM on a variety of webpage understanding tasks, including Attribute Extraction, Open Information Extraction, and Question Answering.
  arXiv  Detail & Related papers  (2022-01-25T20:10:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.