Summary of Revolutionizing Retrieval-augmented Generation with Enhanced Pdf Structure Recognition, by Demiao Lin (chatdoc.com)
Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition
by Demiao Lin
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the effectiveness of Retrieval-Augmented Generation (RAG) in professional knowledge-based question answering. With major foundation model companies opening up Embedding and Chat API interfaces, frameworks like LangChain have integrated the RAG process. However, current methods rely on accessing high-quality text corpora, which is limited by the low accuracy of PDF parsing. The authors conducted an empirical RAG experiment across hundreds of questions from real-world professional documents. They developed a RAG system called ChatDOC, equipped with a panoptic and pinpoint PDF parser. Results show that ChatDOC retrieves more accurate and complete segments, leading to better answers. The system outperformed the baseline on nearly 47% of questions, tied for 38%, and fell short on only 15%. This research has implications for revolutionizing RAG with enhanced PDF structure recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a way that computers answer questions using text from professional documents. But there’s a problem – these documents are often stored in PDF files, which are hard to read correctly. The authors wanted to see if they could do better than current methods by creating a new system called ChatDOC. They tested it on hundreds of real-world questions and found that it was much more accurate than usual. In fact, it did well on almost half the questions, and only got stuck on a small number. This is important because it could help computers answer questions more correctly in the future. |
Keywords
* Artificial intelligence * Embedding * Parsing * Question answering * Rag * Retrieval augmented generation
