FreeChunker: A Cross-Granularity Chunking Framework

TL;DR

FreeChunker introduces a flexible, cross-granularity encoding framework for chunking in RAG systems, improving adaptability and efficiency by treating sentences as atomic units and supporting arbitrary combinations.

Contribution

It shifts from static boundary-based chunking to a flexible retrieval approach that treats sentences as atomic units, reducing computational overhead and enhancing query adaptability.

Findings

Outperforms existing chunking methods in retrieval accuracy.

Reduces computational overhead for semantic boundary detection.

Enhances adaptability to complex queries.

Abstract

Chunking strategies significantly impact the effectiveness of Retrieval-Augmented Generation (RAG) systems. Existing methods operate within fixed-granularity paradigms that rely on static boundary identification, limiting their adaptability to diverse query requirements. This paper presents FreeChunker, a Cross-Granularity Encoding Framework that fundamentally transforms the traditional chunking paradigm: the framework treats sentences as atomic units and shifts from static chunk segmentation to flexible retrieval supporting arbitrary sentence combinations. This paradigm shift not only significantly avoids the computational overhead required for semantic boundary detection, but also enhances adaptability to complex queries. Experimental evaluation on LongBench V2 demonstrates that FreeChunker possesses significant advantages in both retrieval performance and time efficiency compared to existing chunking methods. The pre-trained models and codes are available at https://github.com/mazehart/FreeChunker.