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

• URL: http://arxiv.org/abs/2501.17039v1
• Date: Tue, 28 Jan 2025 16:03:52 GMT
• Title: Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models
• Authors: Minghan Li, Eric Gaussier, Guodong Zhou,
• Abstract summary: 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. We aggregate the query-block relevance scores through a weighted sum method, yielding a comprehensive score for the query with the entire document.
• Score: 24.02950598944251
• License:
• Abstract: In recent years, large language models (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.

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