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 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 Recur...
| Authors | Sovandara Chhoun, Pichdara Po, Sereiwathna Ros et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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