Ext2Gen: Alignment through Unified Extraction and Generation for Robust Retrieval-Augmented Generation

RAG has proven its effectiveness in reducing hallucinations We go beyond accurate retrieval to emphasize in LLMs, when their knowledge is incomplete, robust generation that remains resilient to forgetting outdated, or lacks sufficient detail to accurately and distraction by the two challenges. Our key address specific queries (Gao et al., 2023b; idea for enhancing robustness is an extract-thengenerate Fan et al., 2024). A critical aspect of RAG is the approach, Ext2Gen, where the model "retrieval" process, which involves identifying and first extracts query-relevant sentences from the retrieved selecting relevant text chunks. The quality of these chunks and then refine the information to retrieved chunks plays a pivotal role in the overall generate a precise answer. The extraction step here performance of RAG, as they form the basis serves as a chain-of-thought (CoT) process (Wei for generating factual and contextually relevant answers et al., 2022; Chu et al., 2023), where the model provides aligned with the query intent (Asai et al., the evidence first before generating the final 2024; Wang et al., 2023; Zhang et al., 2024).