An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents

TL;DR

This paper introduces an agentic AI system utilizing LLMs and reinforcement learning to improve the accuracy and adaptability of extracting data from form-like documents, surpassing traditional monolithic methods.

Contribution

It presents a modular multi-agent framework with RL-driven self-improvement capabilities for document data extraction, addressing limitations of existing monolithic LLM-based approaches.

Findings

Effective handling of diverse document formats and layouts

Promising results on SOIRE and CORD benchmark datasets

Self-corrective system reduces extraction errors over time

Abstract

Extracting alphanumeric data from form-like documents such as invoices, purchase orders, bills, and financial documents is often performed via vision (OCR) and learning algorithms or monolithic pipelines with limited potential for systemic improvements. We propose an agentic AI system that leverages Large Language Model (LLM) agents and a reinforcement learning (RL) driver agent to automate consistent, self-improving extraction under LLM inference uncertainty. Our work highlights the limitations of monolithic LLM-based extraction and introduces a modular, multi-agent framework with task-specific prompts and an RL policy of rewards and penalties to guide a meta-prompting agent to learn from past errors and improve prompt-based actor agents. This self-corrective adaptive system handles diverse documents, file formats, layouts, and LLMs, aiming to automate accurate information extraction without the need for human intervention. Results as reported on two benchmark datasets of SOIRE, and CORD, are promising for the agentic AI framework.