A Comparative Study of Language Models for Khmer Retrieval-Augmented Question Answering
A new study explores the effectiveness of Retrieval-Augmented Generation (RAG) for the Khmer language, a low-resource, non-Latin script. Researchers benchmarked three embedding models for dense retrieval, finding BGE-M3 to be the top performer. They then evaluated five generator models, noting that no single model excelled across all metrics, with Qwen3.5-9B leading in faithfulness and context relevance, Qwen3-8B in factual correctness, and SeaLLMs-v3-7B-Chat in answer relevance and correctness. AI
IMPACT Highlights retriever choice as a bottleneck for RAG in low-resource languages, guiding future development for non-Latin scripts.