White Papers

A novel approach for enhancing the performance of large-scale language models by incorporating a fine-tuning technique that leverages task-specific data. This method significantly improves the models' ability to generalize across various tasks, demonstrating superior results compared to traditional fine-tuning methods.

BoostER is a cost effective framework that leverages LLMs to enhance entity resolution by reducing uncertainty in matching records. By using a tailored algorithm and integrating LLM responses, BoostER optimizes the selection of matching questions within a budget, making high-quality entity resolution accessible to small companies and individual users.

Walmart has developed a deep learning-based system to identify "lookalike" customers for it's e-commerce marketing campaigns, utilizing a two-tower architecture to generate customer embeddings from diverse data sources. This scalable solution enhances marketing reach and aims to increase revenue and customer engagement.