English(EN) Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents

递归切块在柬埔寨语农业文献RAG中表现优异

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-21 09:06

研究人员评估了四种文本切块策略，用于一个检索增强生成（RAG）框架，并使用了柬埔寨语农业文献。研究发现，基于字符的递归切块方法，切块大小为300个字符，表现最佳。该方法实现了最低的L2距离和最高的答案相关性及柬埔寨语交并比（IoU）得分，与基于句子的方法相比有显著改进。 AI

影响提高了低资源语言的RAG性能，可能在专业领域实现更好的信息获取。

排序理由学术论文，详细介绍了针对特定语言和领域的文本切块策略评估。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Sovandara Chhoun, Pichdara Po, Sereiwathna Ros, Wan-Sup Cho, Saksonita Khoeurn · 2026-05-22 04:00

Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents

arXiv:2605.22203v1 Announce Type: new Abstract: In this study, we compare the performance of four text chunking approaches: Recursive, Khmer-Aware, Sentence-Based, and LLM-Based within a Retrieval-Augmented Generation (RAG) framework applied to Khmer agricultural documents. The d…
arXiv cs.CL TIER_1 English(EN) · Saksonita Khoeurn · 2026-05-21 09:06

Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents

In this study, we compare the performance of four text chunking approaches: Recursive, Khmer-Aware, Sentence-Based, and LLM-Based within a Retrieval-Augmented Generation (RAG) framework applied to Khmer agricultural documents. The document chunks are encoded using the BGE-M3 mult…