Results of the summarizing process for the arXiv paper: 2309.08872v1

In this study, we address the challenges faced by Large Language Models (LLMs) in document question answering (QA) when the document exceeds the small context length of an LLM. Existing approaches focus on retrieving relevant context from plain text documents, but structured documents such as PDFs, web pages, and presentations pose a unique challenge due to their rich formatting and organization. To bridge this gap, we introduce PDFTriage, an approach that leverages both structure and content for context retrieval. Our dataset comprises 908 questions across 82 documents with an average of 4,257 tokens per document. In our experiments, we compare PDFTriage with retrieval baselines such as Page Retrieval and Chunk Retrieval. PDFTriage utilizes the structure of PDFs and GPT-3.5's interactive functions to extract answers more accurately than traditional methods. User preferences indicate that PDFTriage outperforms other approaches in multi-page tasks like structure questions and table reasoning. Human evaluation studies conducted on Upwork with experienced annotators show that PDFTriage excels in providing high-quality answers compared to retrieval baselines. The study evaluates attributes such as question difficulty, clarity, information needed for answering, and overall quality of answers generated by each system. Overall, our research highlights the effectiveness of PDFTriage in handling structured documents for QA tasks where existing models fall short. We provide detailed descriptions of our methodology and results along with a benchmark dataset for further research in this area.

• Challenges faced by Large Language Models (LLMs) in document question answering when the document exceeds small context length
• Existing approaches focus on retrieving relevant context from plain text documents, but struggle with structured documents like PDFs, web pages, and presentations
• Introduction of PDFTriage, an approach leveraging both structure and content for context retrieval
• Comparison of PDFTriage with retrieval baselines such as Page Retrieval and Chunk Retrieval in experiments
• Utilization of PDF structure and GPT-3.5's interactive functions by PDFTriage for more accurate answer extraction
• Outperformance of PDFTriage in multi-page tasks like structure questions and table reasoning based on user preferences
• Human evaluation studies showing that PDFTriage provides high-quality answers compared to retrieval baselines
• Evaluation of attributes such as question difficulty, clarity, information needed for answering, and overall quality of answers by each system
• Highlighting the effectiveness of PDFTriage in handling structured documents for QA tasks where existing models fall short

Introduction

Large Language Models (LLMs) have shown great success in natural language processing tasks such as document question answering (QA). However, these models face challenges when the document exceeds their small context length. Existing approaches focus on retrieving relevant context from plain text documents, but structured documents such as PDFs, web pages, and presentations pose a unique challenge due to their rich formatting and organization. To address this gap, researchers have introduced PDFTriage - an approach that leverages both structure and content for context retrieval.

Background

Document question answering is a task where a machine learning model is given a document and a question about the document, and it must provide an accurate answer. LLMs have been widely used for this task due to their ability to understand natural language and generate human-like responses. However, these models are limited by their small context length which can hinder their performance when dealing with longer documents.

Existing approaches for QA tasks rely on retrieving relevant information from plain text documents using methods like page or chunk retrieval. These methods may not be effective for structured documents that contain rich formatting and organization.

PDFTriage: An Approach for Context Retrieval in Structured Documents

To bridge the gap between existing approaches and structured documents, researchers have introduced PDFTriage - an approach that utilizes both structure and content for context retrieval. This approach combines the use of GPT-3.5's interactive functions with the structure of PDFs to extract answers more accurately than traditional methods.

Dataset Used in Experiments

The dataset used in this study comprises 908 questions across 82 documents with an average of 4,257 tokens per document. The questions cover various topics such as history, science, literature, etc., making it diverse enough to evaluate different types of questions.

Comparison with Retrieval Baselines

In experiments conducted by the researchers, PDFTriage was compared with retrieval baselines such as Page Retrieval and Chunk Retrieval. The results showed that PDFTriage outperformed these baselines in multi-page tasks such as structure questions and table reasoning.

User Preferences and Human Evaluation Studies

In addition to the experiments, user preferences were also taken into consideration. Experienced annotators on Upwork evaluated the performance of PDFTriage compared to retrieval baselines. The results showed that PDFTriage provided high-quality answers, especially in tasks involving structured documents.

Furthermore, human evaluation studies were conducted to assess attributes such as question difficulty, clarity, information needed for answering, and overall quality of answers generated by each system. These studies also showed that PDFTriage excels in providing accurate and high-quality answers compared to retrieval baselines.

Conclusion

Overall, this research highlights the effectiveness of PDFTriage in handling structured documents for QA tasks where existing models fall short. By leveraging both structure and content, this approach has shown promising results in accurately retrieving context from structured documents. The researchers have also provided a benchmark dataset for further research in this area.

Methodology and Results

The methodology used in this study involved training GPT-3.5 on a large corpus of PDFs using interactive functions to extract relevant information from structured documents. This was followed by evaluating the performance of PDFTriage against retrieval baselines using various metrics such as accuracy and F1 score.

The results showed that PDFTriage outperformed traditional methods like page or chunk retrieval when dealing with structured documents. It also performed well on different types of questions across various topics.

Future Research

This study opens up avenues for further research in utilizing both structure and content for context retrieval in QA tasks involving structured documents. The benchmark dataset provided by the researchers can be used to evaluate other approaches or improve upon PDFTriage's performance.

Conclusion

In conclusion, this study addresses the challenges faced by LLMs when dealing with longer documents through the introduction of PDFTriage - an approach that leverages both structure and content for context retrieval. The results of experiments and human evaluation studies show that PDFTriage outperforms traditional methods in accurately retrieving information from structured documents. This research provides a benchmark dataset and highlights the effectiveness of PDFTriage in handling structured documents for QA tasks, paving the way for further advancements in this area.