arXiv:2605.22099v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for grounding large language model (LLM) outputs in retrieved evidence, thereby reducing hallucination and improving factual accuracy. Its efficacy, however, remains largely unexamined for low-resource, non-Latin-script languages such as Khmer. In this paper, we present a RAG-based question answering system for Khmer-language telecom-domain documents. We conduct a two-phase comparative evaluation. First, we benchmark three embedding models: BGE-M3 (567M), Jina-Embeddings-v3
The proliferation of RAG systems is prompting research into their efficacy across diverse linguistic contexts, especially for languages outside dominant training sets.
This research highlights the critical need to adapt and evaluate advanced AI techniques for low-resource languages, fostering broader AI inclusivity and capability.
We now have specific comparative data on embedding models for Khmer RAG, indicating progress in making advanced AI accessible to non-Latin script, low-resource languages.
- · Khmer-speaking communities
- · Developers of multilingual AI models
- · Research institutions in NLP
- · Generic, unoptimized LLM applications in diverse language contexts
Improved performance of AI-powered information retrieval for Khmer language users, reducing digital information disparities.
Increased investment and development in AI infrastructure and tools for other low-resource languages, spurring a more inclusive global AI ecosystem.
Potential for national-level AI strategies in Southeast Asia to prioritize the development of local language AI, reducing reliance on foreign models.
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Read at arXiv cs.CL