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Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tendency to produce inaccurate "hallucinated" content. However, the evaluation of RAG systems is challenging, as existing benchmarks are limited in scope and diversity. Most of the current benchmarks predominantly assess question-answering applications, overlooking the broader spectrum of situations where RAG could prove advantageous. Moreover, they only evaluate the performance of the LLM component of the RAG pipeline in the experiments, and neglect the influence of the retrieval component and the external knowledge database. To address these issues, this paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios. Specifically, we have categorized the range of RAG applications into four distinct types-Create, Read, Update, and Delete (CRUD), each representing a unique use case. "Create" refers to scenarios requiring the generation of original, varied content. "Read" involves responding to intricate questions in knowledge-intensive situations. "Update" focuses on revising and rectifying inaccuracies or inconsistencies in pre-existing texts. "Delete" pertains to the task of summarizing extensive texts into more concise forms. For each of these CRUD categories, we have developed comprehensive datasets to evaluate the performance of RAG systems. We also analyze the effects of various components of the RAG system, such as the retriever, the context length, the knowledge base construction, and the LLM. Finally, we provide useful insights for optimizing the RAG technology for different scenarios., Comment: 26 Pages

beta-Mannans are hemicelluloses that are abundant in modern diets as components in seed endosperms and common additives in processed food. Currently, the collective understanding of beta-mannan saccharification in the human colon is limited to a few keystone species, which presumably liberate low-molecular-weight mannooligosaccharide fragments that become directly available to the surrounding microbial community. Here, we show that a dominant butyrate producer in the human gut, Faecalibacterium prausnitzii, is able to acquire and degrade various beta-mannooligosaccharides (beta-MOS), which are derived by the primary mannanolytic activity of neighboring gut microbiota. Detailed biochemical analyses of selected protein components from their two beta-MOS utilization loci (F. prausnitzii beta-MOS utilization loci [FpMULs]) supported a concerted model whereby the imported beta-MOS are stepwise disassembled intracellularly by highly adapted enzymes. Coculturing experiments of F. prausnitzii with the primary degraders Bacteroides ovatus and Roseburia intestinalis on polymeric beta-mannan resulted in syntrophic growth, thus confirming the high efficiency of the FpMULs' uptake system. Genomic comparison with human F. prausnitzii strains and analyses of 2,441 public human metagenomes revealed that FpMULs are highly conserved and distributed worldwide. Together, our results provide a significant advance in the knowledge of beta-mannan metabolism and the degree to which its degradation is mediated by cross-feeding interactions between prominent beneficial microbes in the human gut. IMPORTANCE Commensal butyrate-producing bacteria belonging to the Firmicutes phylum are abundant in the human gut and are crucial for maintaining health. Currently, insight is lacking into how they target otherwise indigestible dietary fibers and into the trophic interactions they establish with other glycan degraders in the competitive gut environment. By combining cultivation, genomic, and detailed, QC 20211015