The field of retrieval-augmented generation is moving towards more efficient and effective methods of incorporating external knowledge into language models. Recent developments have focused on addressing challenges related to factual correctness, source attribution, and response completeness. Modular pipelines and graph-centric frameworks have been proposed to improve the performance and applicability of retrieval-augmented generation systems. Additionally, there is a growing interest in representing n-ary relational facts and hypergraph-structured knowledge to better model complex relationships in real-world data. Noteworthy papers include:

• GINGER, which achieves state-of-the-art performance on the TREC RAG'24 dataset with its modular pipeline for grounded response generation.
• RGL, a graph-centric framework that accelerates the prototyping process and enhances the performance of graph-based retrieval-augmented generation systems.
• HyperGraphRAG, a novel hypergraph-based retrieval-augmented generation method that outperforms standard RAG and GraphRAG in accuracy and generation quality.