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Document Haystack Dataset

This repository contains the dataset for the paper β€œDocument Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark”.


πŸ“‘ Abstract Paper

The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To address this, we introduce Document Haystack, a comprehensive benchmark designed to evaluate the performance of Vision Language Models (VLMs) on long, visually complex documents. Document Haystack features documents ranging from 5 to 200 pages and strategically inserts pure text or multimodal text+image "needles" at various depths within the documents to challenge VLMs' retrieval capabilities. Comprising 400 document variants and a total of 8,250 questions, it is supported by an objective, automated evaluation framework. We detail the construction and characteristics of the Document Haystack dataset, present results from prominent VLMs and discuss potential research avenues in this area.


πŸ—‚οΈ Overview

Document Haystack is a comprehensive benchmark dataset designed to evaluate the long-context retrieval and multimodal document understanding capabilities of Vision Language Models (VLMs).

It expands on the Needle in a Haystack concept by embedding needles β€” short key-value statements in pure text or as multimodal text+image snippets β€” within real-world long documents (5–200 pages). These needles test whether models can locate specific information hidden deep inside long, complex documents with textual, visual or mixed content.


🎯 Benchmark Design

  • Key-value pairs: Each needle follows the pattern β€œThe secret KEY is VALUE.” where VALUE appears as either text or an image. For example: β€œThe secret sport is basketball.”. The keys span diverse categories including sports, animals, currencies, fruits, musical instruments, and more (see Table 3 in the paper for the complete category list).
  • Tasks: Each needle has an associated retrieval question: β€œWhat is the secret KEY in the document?”
  • Objective scoring: The VLM’s answer is checked for the correct VALUE (or acceptable aliases for text+image needles).

βœ… Key Features

  • Document Lengths: 5, 10, 25, 50, 75, 100, 150, 200 pages
  • Total Documents: 400 document variants
  • Total Questions: 8,250 unique retrieval queries
  • Needle Types:
    • Text Needles: Pure text (e.g., β€œThe secret sport is basketball.”)
    • Text+Image Needles: The value is shown as an image (e.g., β€œThe secret sport is <image of basketball>.”)
  • Formats Provided:
    • Original PDF
    • Page-wise images (200 DPI)
    • Parsed plain text (for text needles only)

πŸ”¬ Use Cases

  • Stress-test multimodal VLMs for long-context understanding
  • Compare retrieval from parsed pdf text vs images vs original pdfs
  • Explore text vs image vs mixed retrieval challenges
  • Measure performance drop with increasing context length

πŸ“¦ Document Haystack Format Variants

Benchmark Set Format Description Use Case
(1) Text needles PDF Original document format VLMs supporting PDF input
Image 200 DPI page-wise images VLMs requiring image input
Text Extracted plain text Text-only LLMs
(2) Text+Image needles PDF Original document format VLMs supporting PDF input
Image 200 DPI page-wise images VLMs requiring image input

πŸ“Š Document Haystack Characteristics

# Pages 5 10 25 50 75 100 150 200 Total
Text Needles # Documents 25 25 25 25 25 25 25 25 200
Text Needles # Questions 125 250 625 625 625 625 625 625 4125
Text+Image Needles # Documents 25 25 25 25 25 25 25 25 200
Text+Image Needles # Questions 125 250 625 625 625 625 625 625 4125
Total Documents 50 50 50 50 50 50 50 50 400
Total Questions 250 500 1250 1250 1250 1250 1250 1250 8250

πŸ“ Dataset Structure

Below is an example of the dataset’s folder layout:

DocumentHaystack/
β”œβ”€β”€ AIG/
β”‚   β”œβ”€β”€ AIG_5Pages/
β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_ImageNeedles.pdf
β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_TextNeedles.pdf
β”‚   β”‚   β”œβ”€β”€ Images_TextImageNeedles/
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_ImageNeedles_page_1.jpg
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_ImageNeedles_page_2.jpg
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”œβ”€β”€ Images_TextNeedles/
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_TextNeedles_page_1.jpg
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_TextNeedles_page_2.jpg
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”œβ”€β”€ Text_TextNeedles/
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_TextNeedles_page_1.txt
β”‚   β”‚   β”‚   β”œβ”€β”€ AIG_5Pages_TextNeedles_page_2.txt
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”œβ”€β”€ needles_info.csv
β”‚   β”‚   β”œβ”€β”€ needles.csv
β”‚   β”‚   β”œβ”€β”€ prompt_questions.txt
β”‚   β”œβ”€β”€ AIG_10Pages/
β”‚   β”œβ”€β”€ AIG_25Pages/
β”‚   β”œβ”€β”€ AIG_50Pages/
β”‚   β”œβ”€β”€ AIG_75Pages/
β”‚   β”œβ”€β”€ AIG_100Pages/
β”‚   β”œβ”€β”€ AIG_150Pages/
β”‚   β”œβ”€β”€ AIG_200Pages/
β”‚
β”‚   needles.csv
β”‚   prompt_questions.txt
β”‚
β”œβ”€β”€ AmericanAirlines/
β”œβ”€β”€ APA/
β”œβ”€β”€ BankOfMontreal/
...

πŸ“ File and Folder Descriptions

Below is an explanation of the files inside the AIG_5Pages subfolder:

File/Folder Description
AIG_5Pages_ImageNeedles.pdf PDF version with hidden Text+Image needles
AIG_5Pages_TextNeedles.pdf PDF version with hidden Text-only needles
Images_TextImageNeedles/ Folder with page-wise JPGs of the Text+Image needles PDF
Images_TextNeedles/ Folder with page-wise JPGs of the Text-only needles PDF
Text_TextNeedles/ Folder with plain .txt files per page for Text-Needles version
needles.csv Lists the key-value pairs inserted in the document variant
needles_info.csv Detailed placement metadata for each needle (page, coordinates, font, etc.)
prompt_questions.txt Contains the questions the model must answer for needle retrieval

Within the main AIG folder, you'll find two key files: needles.csv, which lists all 25 needles that are utilized across the different AIG variants, and prompt_questions.txt, which contains the complete set of 25 prompts used throughout the AIG variants.

There are 25 top-level subfolders in total, each referring to a different document (e.g., AIG/, AmericanAirlines/, APA/, BankOfMontreal/), each structured the same way.


πŸ“Œ needles_info.csv

Each document variant includes a needles_info.csv detailing every needle’s properties:

Example rows:

The secret currency is a "euro".,1,13,purple,white,0.546,0.163,times-roman,143
The secret office supply is a "pencil".,2,8,gray,white,0.339,0.931,times-bold,90
Column Description
Needle Needle text statement (the hidden key-value pair)
Page Page number where needle is inserted
Font Size Font size
Text Color Foreground color
Background Color Background color
X X coordinate (normalized 0–1)
Y Y coordinate (normalized 0–1)
Font Font type
Scale Image scale (for text+image needles)

Placement:

  • Needles are randomly placed across equal, non-overlapping page ranges to ensure coverage throughout the document.
  • Same locations are reused for both text-only and text+image sets.

πŸ“ Reference Evaluation

Use with the Document Haystack Benchmark Code for:

  • Fully automated inference & scoring pipelines
  • Heatmap generation for depth-based performance

πŸ“ License

This project is licensed under the CC-BY-NC-4.0 License - see the GitHub LICENSE file for details.


πŸ‘₯ Authors

Amazon AGI

  • Goeric Huybrechts
  • Srikanth Ronanki
  • Sai Muralidhar Jayanthi
  • Jack Fitzgerald
  • Srinivasan Veeravanallur
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