Mitigating Hallucination with ZeroG: An Advanced Knowledge Management Engine

TL;DR

By employing a black-box distillation approach, ZeroG creates a distilled dataset without relying on intermediate features, optimizing computational efficiency, and improves the accuracy of question-and-answer systems.

Abstract

The growth of digital documents presents significant challenges in efficient management and knowledge extraction. Traditional methods often struggle with complex documents, leading to issues such as hallucinations and high latency in responses from Large Language Models (LLMs). ZeroG, an innovative approach, significantly mitigates these challenges by leveraging knowledge distillation and prompt tuning to enhance model performance. ZeroG utilizes a smaller model that replicates the behavior of a larger teacher model, ensuring contextually relevant and grounded responses, by employing a black-box distillation approach, it creates a distilled dataset without relying on intermediate features, optimizing computational efficiency. This method significantly enhances accuracy and reduces response times, providing a balanced solution for modern document management. Incorporating advanced techniques for document ingestion and metadata utilization, ZeroG improves the accuracy of question-and-answer systems. The integration of graph databases and robust metadata management further streamlines information retrieval, allowing for precise and context-aware responses. By transforming how organizations interact with complex data, ZeroG enhances productivity and user experience, offering a scalable solution for the growing demands of digital document management.

Figures (4)

• Figure 1: ZeroG - Document Ingestion and Distilled Dataset Generation Pipeline
• Figure 2: ZeroG - Response Retrieval Pipeline
• Figure 3: ZeroG - Knowledge Distillation: Generating Distilled Data
• Figure 4: ZeroG - Knowledge Distillation: RAG pipeline