Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2406.00456v1
• Date: Sat, 1 Jun 2024 14:45:03 GMT
• Title: Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation
• Authors: Zijie Zhong, Hanwen Liu, Xiaoya Cui, Xiaofan Zhang, Zengchang Qin,
• Abstract summary: We introduce Mix-of-Granularity (MoG), a method that determines the optimal granularity of a knowledge database based on input queries using a router. We extend MoG to Mix-of-Granularity-Graph (MoGG), where reference documents are pre-processed into graphs, enabling the retrieval of relevant information from distantly situated chunks.
• Score: 7.071677694758966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating information from different reference data sources is a major challenge for Retrieval-Augmented Generation (RAG) systems because each knowledge source adopts a unique data structure and follows different conventions. Retrieving from multiple knowledge sources with one fixed strategy usually leads to under-exploitation of information. To mitigate this drawback, inspired by Mix-of-Expert, we introduce Mix-of-Granularity (MoG), a method that dynamically determines the optimal granularity of a knowledge database based on input queries using a router. The router is efficiently trained with a newly proposed loss function employing soft labels. We further extend MoG to Mix-of-Granularity-Graph (MoGG), where reference documents are pre-processed into graphs, enabling the retrieval of relevant information from distantly situated chunks. Extensive experiments demonstrate that both MoG and MoGG effectively predict optimal granularity levels, significantly enhancing the performance of the RAG system in downstream tasks. The code of both MoG and MoGG will be made public.

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