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HtmlRAG HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
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The paper "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems" explores the use of HTML as the format for retrieved knowledge in Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems convert HTML documents to plain text, which leads to the loss of structural and semantic information. The authors propose using HTML directly to preserve this information, arguing that large language models (LLMs) are capable of understanding HTML without additional fine-tuning.
Introduction
The paper "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems" explores the use of HTML as the format for retrieved knowledge in Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems convert HTML documents to plain text, which leads to the loss of structural and semantic information. The authors propose using HTML directly to preserve this information, arguing that large language models (LLMs) are capable of understanding HTML without additional fine-tuning.
Key Points
- Information Loss in Plain Text Conversion: Converting HTML to plain text results in the loss of structural and semantic information, such as headings and table structures. This can lead to disordered content and the loss of important tags.
- Advantages of HTML: Using HTML as the format for external knowledge in RAG systems preserves the information inherent in HTML documents. LLMs have encountered HTML documents during pre-training and possess the ability to understand HTML without further fine-tuning.
- Challenges and Solutions: HTML contains additional content like tags, JavaScript, and CSS, which can introduce noise and increase input tokens. The authors propose HTML cleaning, compression, and pruning strategies to shorten the HTML while minimizing information loss.
- Experimental Validation: The authors conducted experiments on six QA datasets, demonstrating the superiority of using HTML in RAG systems. They also performed ablation studies to validate the effectiveness of each component in their proposed method.
Conclusion
The paper concludes that using HTML as the format for external knowledge in RAG systems is more effective than using plain text. The proposed HTML cleaning and pruning strategies successfully reduce the length of HTML documents while retaining key information, leading to improved performance in various QA tasks.
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