DocHRL framework uses reinforcement learning for cost-optimized document classification

Researchers have developed DocHRL, a novel hierarchical reinforcement learning framework designed to optimize document classification costs. This system adaptively selects the most efficient classification policy for each document, considering factors like complexity, model inference costs, misclassification penalties, and human labeling expenses. By employing a two-level policy hierarchy, DocHRL can choose between vision classifiers, LLMs, OCR, and human review, leading to improved classification performance and operational efficiency. AI

IMPACT This framework could significantly reduce operational costs in document processing by intelligently allocating computational and human resources.

RANK_REASON The cluster contains a research paper detailing a new framework for document classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DocHRL
• LLMs
• OCR
• Proximal Policy Optimisation
• Reinforcement Learning
• RVL-CDIP benchmark

• paper
• infra

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