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arXiv · 2605.22203

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

Sovandara Chhoun·Pichdara Po·Sereiwathna Ros·Wan-Sup Cho·Saksonita Khoeurn
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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 document chunks are encoded using the BGE-M3 multilingual embedding model and retrieved using the FAISS library. Performance is evaluated using four metrics: Average Retrieval Score (L2 distance), Answer Relevance, Khmer Coverage, and Khmer Intersection over Union, all measured against ground-truth question-answer pairs. For evaluation, we perform 5-fold cross-validation over 18 question-answer pairs. We observe the best performance for the character-based Recursive chunking method with a chunk size of 300 characters, achieving the lowest L2 distance (0.4295 +- 0.0461), highest Answer Relevance (0.8663 +- 0.0199), and highest Khmer IoU (0.6441 +- 0.0347). A paired t-test shows a statistically significant improvement over the Sentence-Based chunking method in L2 distance (p = 0.0121). These results highlight the importance of segmentation granularity and structural preservation for optimizing dense retrieval in morphologically complex, low-resource languages such as Khmer.

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Sovandara Chhoun, Pichdara Po, Sereiwathna Ros, Wan-Sup Cho, Saksonita Khoeurn. 2026-05-21. Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents. https://arxiv.org/abs/2605.22203

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