Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models

Abstract: In recent years, LLMs have demonstrated exceptional power in various domains, including information retrieval. Most of the previous practices involve leveraging these models to create a single embedding for each query, each passage, or each document individually, a strategy exemplified and used by the Retrieval-Augmented Generation (RAG) framework. While this method has proven effective, we argue that it falls short in fully capturing the nuanced intricacies of document-level texts due to its reliance on a relatively coarse-grained representation. To address this limitation, we introduce a novel, fine-grained approach aimed at enhancing the accuracy of relevance scoring for long documents. Our methodology firstly segments a long document into blocks, each of which is embedded using an LLM, for matching with the query representation. When calculating the relevance score, we aggregate the query-block relevance scores through a weighted sum method, yielding a comprehensive score for the query with the entire document. Despite its apparent simplicity, our experimental findings reveal that this approach outperforms standard representation methods and achieves a significant reduction in embedding generation latency. Moreover, by carefully optimizing pairwise loss functions, superior performances have been achieved.