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.

翻译：数字文档的增长对高效管理和知识提取提出了重大挑战。传统方法在处理复杂文档时常常力不从心，导致大型语言模型（LLMs）出现幻觉和高延迟响应等问题。ZeroG作为一种创新方法，通过利用知识蒸馏和提示调优来提升模型性能，显著缓解了这些挑战。ZeroG采用一个较小的模型来复现较大教师模型的行为，通过黑盒蒸馏方法，在不依赖中间特征的情况下创建蒸馏数据集，从而优化计算效率，确保生成上下文相关且基于事实的响应。该方法显著提高了准确性并缩短了响应时间，为现代文档管理提供了平衡的解决方案。ZeroG结合了先进的文档摄取和元数据利用技术，提升了问答系统的准确性。图数据库与稳健的元数据管理的集成进一步简化了信息检索，实现了精确且上下文感知的响应。通过改变组织与复杂数据的交互方式，ZeroG提高了生产力和用户体验，为日益增长的数字文档管理需求提供了可扩展的解决方案。