  Large Language Models (LLMs) have made significant strides in information
acquisition. However, their overreliance on potentially flawed parametric
knowledge leads to hallucinations and inaccuracies, particularly when handling
long-tail, domain-specific queries. Retrieval Augmented Generation (RAG)
addresses this limitation by incorporating external, non-parametric knowledge.
Nevertheless, the retrieved long-context documents often contain noisy,
irrelevant information alongside vital knowledge, negatively diluting LLMs'
attention. Inspired by the supportive role of essential concepts in
individuals' reading comprehension, we propose a novel concept-based RAG
framework with the Abstract Meaning Representation (AMR)-based concept
distillation algorithm. The proposed algorithm compresses the cluttered raw
retrieved documents into a compact set of crucial concepts distilled from the
informative nodes of AMR by referring to reliable linguistic features. The
concepts explicitly constrain LLMs to focus solely on vital information in the
inference process. We conduct extensive experiments on open-domain
question-answering datasets to empirically evaluate the proposed method's
effectiveness. The results indicate that the concept-based RAG framework
outperforms other baseline methods, particularly as the number of supporting
documents increases, while also exhibiting robustness across various backbone
LLMs. This emphasizes the distilled concepts are informative for augmenting the
RAG process by filtering out interference information. To the best of our
knowledge, this is the first work introducing AMR to enhance the RAG,
presenting a potential solution to augment inference performance with
semantic-based context compression.
