Datasets for \Learning to Choose: Self-Selection Boosts Knowledge Fusion in RAG\

We propose a novel self-Selection RAG fram framework, where the LLM is made to select from pairwise responses generated with internal parametric knowledge solely and with external retrieved knowledge together to achieve enhanced performance. To this end, we devise a self-Selection-RGP method to enhance the capabilities of the LLM in both generating and choosing the correct answer, by training the LLM with Direct Preference Optimization (DPO) over a curated Retrieval-Generation Preference (RGP) dataset.