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Step 2: LLM-based Triage (frame selection/filling) |
Question: "Can you summarize the key takeaways from pages 5-7?" |
Document Context: |
Pages [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] |
... |
Section Title: "2 Related Works" Pages: [2, 3] |
Section Title: "2.1 Tool and Retrieval Augmented LLMs" Pages: [2] |
… |
Table Caption: "Table 1: GPTriage functions for Document QA" Pages: [4] |
Available Functions: fetch_pages, fetch_section, search, … |
fetch_pages(pages: [5, 6, 7]) |
Step 3: Question answering with selected context |
Question: "Can you summarize the key takeaways from pages 5-7?" |
Page 5: |
…length less than 10 pages, to ensure that there is sufficient but not excessive… |
Page 6: |
…the query embedding. We then feed each page’s text as context for answering… |
Page 7: |
…1. The overall quality of the question, such as its difficulty, clarity,... |
Answer: The key takeaways of ... |
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Figure 1: Overview of the PDFTriage technique: PDFTriage leverages a PDF’s structured metadata to implement a more precise and accurate document question-answering approach. It starts by generating a structured metadata representation of the document, extracting information surrounding section text, figure captions, he... |
Next, given a query, a LLM-based Triage selects the document frame needed for answering the query and retrieves it directly from the selected page, section, figure, or table. Finally, the selected context and inputted query are processed by the LLM before the generated answer is outputted. |
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Document Question Answering |
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. Several datasets have been constructed to benchmark different aspects of document-focused question-answering. DocVQA (Mathew et al., 2021) is a visual question-answering dataset focused that uses document scans. A recent work by Lan-deghem et al. (2023) focused on a dataset for document understanding and evaluation c... |
getting questions that can reference the document structure or content, can be extractive or abstractive, and can require long-form answers or rewrites. |
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3 PDFTriage: Structured Retrieval from Document Metadata |
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The PDFTriage approach consists of three steps to answer a user’s question, shown in Figure 1: |
1. Generate document metadata (Sec. 3.1): Extract the structural elements of a document and convert them into readable metadata. |
2. LLM-based triage (Sec. 3.2): Query the LLM to select the precise content (pages, sections, retrieved content) from the document. |
3. Answer using retrieved content (Sec. 3.3): Based on the question and retrieved content, generate an answer. |
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# of Documents 82 |
# of Questions 908 |
Easy Questions 393 |
Medium Questions 144 |
Hard Questions 266 |
“Unsure” Questions 105 |
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Table 1: Dataset statistics for the PDFTriage evaluation dataset. |
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PDFTriage Document Distribution by Word Count |
Frequency |
10 8 6 4 2 0 |
Document Word Count |
0 10000 20000 30000 40000 |
Avg. Doc. Length |
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Figure 2: PDFTriage Document Distribution by Word Count |
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3.1 Document Representation |
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We consider born-digital PDF documents as the structured documents that users will be interacting with. Using the Adobe Extract API, we convert the PDFs into an HTML-like tree, which allows us to extract sections, section titles, page information, tables, and figures.¹ The Extract API generates a hierarchical tree of e... |
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3.2 LLM Querying of Document |
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PDFTriage utilizes five different functions in the approach: fetch_pages, fetch_sections, |
¹https://developer.adobe.com/document-services/apis/pdf-extract/ |
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