HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge
  in RAG Systems
Retrieval-Augmented Generation (RAG) has been shown to improve knowledge
capabilities and alleviate the hallucination problem of LLMs. The Web is a
major source of external knowledge used in RAG systems, and many commercial
systems such as ChatGPT and Perplexity have used Web search engines as their
major retrieval systems. Typically, such RAG systems retrieve search results,
download HTML sources of the results, and then extract plain texts from the
HTML sources. Plain text documents or chunks are fed into the LLMs to augment
the generation. However, much of the structural and semantic information
inherent in HTML, such as headings and table structures, is lost during this
plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG,
which uses HTML instead of plain text as the format of retrieved knowledge in
RAG. We believe HTML is better than plain text in modeling knowledge in
external documents, and most LLMs possess robust capacities to understand HTML.
However, utilizing HTML presents new challenges. HTML contains additional
content such as tags, JavaScript, and CSS specifications, which bring extra
input tokens and noise to the RAG system. To address this issue, we propose
HTML cleaning, compression, and pruning strategies, to shorten the HTML while
minimizing the loss of information. Specifically, we design a two-step
block-tree-based pruning method that prunes useless HTML blocks and keeps only
the relevant part of the HTML. Experiments on six QA datasets confirm the
superiority of using HTML in RAG systems.
672ad53b6f6797b288015187
Jiejun Tan
claimed_verified
2024-11-15T09:37:15.301Z
62e52483a944e2a56cd2c6ca
https://cdn-avatars.huggingface.co/v1/production/uploads/62e52483a944e2a56cd2c6ca/pG44O-1qD00q5CEJMMyFQ.jpeg
Jiejun Tan
user
zstanjj
672ad53b6f6797b288015188
Zhicheng Dou
672ad53b6f6797b288015189
Wen Wang
672ad53b6f6797b28801518a
Mang Wang
672ad53b6f6797b28801518b
Weipeng Chen
672ad53b6f6797b28801518c
Ji-Rong Wen
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge
  in RAG Systems
Retrieval-Augmented Generation (RAG) has been shown to improve knowledge
capabilities and alleviate the hallucination problem of LLMs. The Web is a
major source of external knowledge used in RAG systems, and many commercial
systems such as ChatGPT and Perplexity have used Web search engines as their
major retrieval systems. Typically, such RAG systems retrieve search results,
download HTML sources of the results, and then extract plain texts from the
HTML sources. Plain text documents or chunks are fed into the LLMs to augment
the generation. However, much of the structural and semantic information
inherent in HTML, such as headings and table structures, is lost during this
plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG,
which uses HTML instead of plain text as the format of retrieved knowledge in
RAG. We believe HTML is better than plain text in modeling knowledge in
external documents, and most LLMs possess robust capacities to understand HTML.
However, utilizing HTML presents new challenges. HTML contains additional
content such as tags, JavaScript, and CSS specifications, which bring extra
input tokens and noise to the RAG system. To address this issue, we propose
HTML cleaning, compression, and pruning strategies, to shorten the HTML while
minimizing the loss of information. Specifically, we design a two-step
block-tree-based pruning method that prunes useless HTML blocks and keeps only
the relevant part of the HTML. Experiments on six QA datasets confirm the
superiority of using HTML in RAG systems.
HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning.
Retrieval-Augmented Generation RAG Web search engines LLMs HTML plain text headings table structures HtmlRAG HTML cleaning compression pruning block-tree-based pruning QA datasets