Improving RAG Systems via Sentence Clustering and Reordering

Large Language Models (LLMs) have gained noteworthy importance and attention across different domains and fields in recent years. Information Retrieval (IR) is one of the domains they impacted the most, as witnessed by the recent increase in the number of IR systems incorporating generative models. Specifically, Retrieval Augmented Generation (RAG) is the emerging paradigm that integrates existing knowledge from large-scale document corpora into the generation process, enabling the model to generate more coherent, contextually relevant, and accurate text across various tasks. Such tasks include summarization, question answering, and dialogue systems. Recent studies have highlighted the significant positional dependence exhibited by RAG systems. Such studies observed how the placement of information within the LLM input prompt drastically affects the generated output. We ground our study on this property by investigating alternative strategies for ordering sentences within the LLM promp...