Hufsa Haq – Report on AI PDF Interrogator Research

 Literature Survey on PDF Interrogation with ChatGPT

In order to extract information from documents and utilise this information for answers, researchers have looked into ChatGPT’s potential as a PDF interrogator.

ChatGPT interacts with users by being given a document as background and providing answers to their inquiries or information based on that PDF. The model produces answers by extracting information from the paper.

Techniques to improve ChatGPT’s document interrogation capabilities have been investigated by researchers. To enhance the model's comprehension of factual data, outside knowledge sources like structured databases or knowledge graphs can be used₁.

ChatGPT’s document interrogation performance has been enhanced through the use of reinforcement learning. The model learns to produce more accurate and detailed responses depending on the document with the use of reward models and reinforcement signals₂.

One of the difficulties in employing ChatGPT for PDF interrogation is the model's tendency to generate convincing but erroneous information. Making sure the generated responses are accurate is still a problem ₃.

Information retrieval systems, chatbot interfaces, and virtual assistants are some examples. To give consumers easy access to reliable information from documents, these systems make use of ChatGPT’s capabilities.

Future studies on ChatGPT-based document interrogation might look into ways to manage unclear questions, improve fact-checking systems, and efficiently incorporate outside knowledge. As a document interrogator that enables data extraction through conversational interactions, ChatGPT has promise. Current research attempts to fix issues and improve ChatGPT’s document interrogation capabilities. There are already websites which exist to train ChatGPT to answer questions about any give document, such as Pickaxe, Myreader and Chat PDF

References:

¹ Dua, D., Wang, S., Dasigi, P., Singh, A., & Gardner, M. (2019). A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. https://aclanthology.org/N19-1246/

³ Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., & Downey, D. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. Https://aclanthology.org/2020.acl-main.740/

²Huang, H., Parnamaa, M., Paperno, D., Galstyan, A., & Das, D. (2020). Improved Few-Shot Text Classification and Language Modelling through Self-Supervised Pretraining with Structured Bilingual Context.

Training for ChatGPT

It normally takes two steps to train ChatGPT to query documents. Pretraining is the first step, in which ChatGPT is trained on a sizable quantity of freely accessible text from the internet. This step aids the model's acquisition of grammar, information, reasoning skills, and a comprehensive knowledge of language.

The second stage, tuning, is carried out on a particular task, such document interrogation. Training ChatGPT on a dataset built especially for PDF interrogation is a necessary step towards fine-tuning. The documents in this collection are accompanied by questions or queries that a user might use to extract data from the documents.

The model is trained using supervised learning and reinforcement learning strategies in combination during fine-tuning. The document and accompanying query are sent to the model, and it learns from them.

Several variations and techniques have been proposed to improve the performance of ChatGPT in document interrogation, including the use of external knowledge sources, attention mechanisms, and reinforcement learning methods. The literature survey will provide an overview of these approaches and their effectiveness in enhancing the document interrogation capabilities of ChatGPT.

There are several tools and libraries which would aid building a PDF interrogator using ChatGPT –

1.      Hugging Face Transformers: Hugging Face provides a powerful library called Transformers that offers a wide range of pretrained language models, including ChatGPT variants. Transformers allows fine-tuning of these models, and provides utilities for integrating the model into your application.

2.      PyPDF2: PyPDF2 is a Python library for working with PDF files. It provides functionalities to extract text, merge multiple PDFs, extract images, and more. PyPDF2 can be helpful in extracting text or specific sections from PDFs to present to the ChatGPT model.

3.      OCR (Optical Character Recognition) Tools: If PDFs contain scanned documents or images, OCR tools are needed to convert the images into machine-readable text.