Boosting RAG: Self-Explaining Contrastive Evidence Re-ranking for Enhanced Factuality

This research explores a novel approach to enhance Retrieval-Augmented Generation (RAG) models, focusing on improving factuality and transparency. The use of self-explaining contrastive evidence re-ranking is a promising technique for better aligning generated text with retrieved information.

• •Focuses on improving the factuality of RAG systems.
• •Employs self-explaining contrastive evidence re-ranking.
• •The approach aims to enhance transparency in the generation process.