Development Background of Webis

• The development background of Webis is the rapid rise of large language models (LLMs), the lowered threshold for local model deployment, and the increasing demand for large language models with personalized, specialized functions.
• Although models like GPT-4 and LLaMA perform excellently, many teams and researchers still face the challenges of models not being specialized enough and having limited data available for training when running on local or private servers.
• Webis is committed to solving these problems by providing a developer-friendly framework for extracting and cleaning data from webpages (HTML) and various formats (DOC, PDF, etc.), offering high-quality input for LLM training.

Industry Background

• AI Application Explosion The popularity of generative AI such as ChatGPT has promoted its wide application in education, medical care, scientific research, and industry. However, general models often fail to meet user expectations in professional fields (such as law, medicine, scientific papers, etc.), creating an urgent need for domain-specific training data.

• High-Quality Data Scarcity The effectiveness of LLMs highly depends on data, but currently, high-quality, structured data is often difficult to obtain. Developers and researchers frequently spend a lot of ineffective time on web crawling, format conversion, and data cleaning rather than focusing on model optimization.

• Open Source and Customization Needs Commercial datasets are expensive and carry copyright and privacy risks. The open-source community and enterprises hope to use tools to build controllable datasets from the open web and own documents, constructing a truly "belonging to oneself" LLM.

Webis's Motivation

1. Lower Data Preparation Threshold

   • One-click extraction of webpages and multiple document formats
   • Automatic cleaning of noisy data, conversion to unified format

2. Facilitate Personalized LLM Training

   • Provide dedicated training corpus for specific domain models
   • Allow researchers and enterprises to quickly build customized datasets

3. Developer-Friendly

   • Provide concise API and command-line tools
   • Combine troubleshooting and performance optimization documentation to reduce learning difficulty

Vision

Webis is not just a data processing tool; it aspires to be a bridge connecting real-world data and large language model training. By simplifying data extraction and cleaning processes, Webis can help:

• Students and researchers: Quickly collect scientific research corpus and explore AI applications in academic fields
• Startups and enterprises: Build private large models that meet their business needs
• Open-source community: Share high-quality data processing solutions and promote the prosperity of the AI ecosystem

Ultimate goal: To make high-quality training data acquisition simpler, more efficient, and more reliable.