Document classification and orientation detection are crucial processes for businesses dealing with vast volumes of diverse documents. Many organizations aim to automate these processes using large language models (LLMs), but encounter limitations due to hallucinations—where LLMs provide inaccurate or fabricated information. Additionally, existing solutions for document classification often use deep learning models, which are resource-intensive, slow to train, and not ideal for processing structured, template-based documents.
RediMinds faced these challenges head-on, aiming to create a solution that balances speed, efficiency, and accuracy in automating document handling while minimizing the impact of hallucinations.
Evaluating the Document Orientation Problem: In this step, we explored the use of traditional machine learning algorithms instead of solely relying on deep learning. By applying digital image processing techniques, we developed a highly efficient mathematical algorithm to detect document orientation. This approach proved particularly effective in identifying incorrectly rotated documents, ensuring high accuracy while maintaining computational efficiency.
Template-Based Document Classification: Rather than using resource-intensive deep learning models, we identified that for template-based documents (e.g., invoices, emails), a simpler machine learning model could be just as effective. By using a collection of five smaller models with specialized tokenization strategies, we trained a custom machine learning model to classify documents with an impressive 97% accuracy. This approach eliminated the need for deep networks with millions of parameters, reducing both computational and time costs.
Addressing Hallucinations in LLMs: One significant challenge in using LLMs for document classification is their tendency to “hallucinate” or fabricate information, particularly when dealing with ambiguous or incomplete data. To combat this, we implemented a hybrid approach that combined LLMs with deterministic rule-based models. This ensured that when the LLM encountered uncertain scenarios, the rule-based models provided concrete, fact-based outputs, reducing hallucination and improving trust in the automated processes.
Benchmarking and Comparison: We set up a comprehensive set of benchmarks to compare various models, documenting their performance across different datasets. We measured speed, accuracy, hallucination rates, and resource consumption for each model, optimizing their solution over time. This iterative approach helped the team continually refine their algorithm, ensuring it met the highest standards of efficiency and reliability.
Collaboration and Integration: Through collaboration with internal teams and external research, we continued to iterate on the solution, identifying opportunities to further streamline processes. Our commitment to sharing findings and working cross-discipline led to breakthroughs in handling other machine learning challenges, such as image recognition and tokenization optimization.
Our final solution combined the best of both worlds—leaner, more efficient machine learning models and traditional, rule-based algorithms to solve the document classification problem. Our system’s ability to classify documents with near-perfect accuracy (97%) and detect misorientations in a resource-efficient manner stands as a testament to the power of iterative model comparison and multi-algorithm strategies.
Additionally, our ability to address LLM hallucinations using a hybrid approach resulted in improved trustworthiness and reliability in document processing, ensuring that organizations could rely on accurate classifications without manual intervention.
- 97% Accuracy in classifying template-based documents, outperforming standard deep learning models.
- 50% Reduction in computational resources, allowing faster document processing at scale.
- Minimal hallucination rates in LLM-based processes, thanks to the hybrid model approach.
- Reduced time-to-deployment, with model training taking significantly less time compared to deep learning models.
- Scalability: The solution can easily be applied to other document processing tasks, such as contract analysis, image-based data extraction, and email filtering.
This case study highlights RediMinds’ novel approach to document classification by leveraging lightweight machine learning models and addressing the limitations of hallucinations in LLMs. Through iterative experimentation, we achieved industry-leading accuracy, computational efficiency, and reliability in handling diverse document types. Our solution paves the way for other organizations to optimize document processing workflows, saving time, resources, and improving overall operational efficiency.