A fine-tuned ensemble of a generative small language model and an embedding model outperformed zero-shot LLMs on Korean search relevance labeling, with reported Cohen's kappa of 0.646 versus 0.387 and 60x lower latency.
Performance of Recent Large Language Models for a Low-Resourced Language
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abstract
Large Language Models (LLMs) have shown significant advances in the past year. In addition to new versions of GPT and Llama, several other LLMs have been introduced recently. Some of these are open models available for download and modification. Although multilingual large language models have been available for some time, their performance on low-resourced languages such as Sinhala has been poor. We evaluated four recent LLMs on their performance directly in the Sinhala language, and by translation to and from English. We also evaluated their fine-tunability with a small amount of fine-tuning data. Claude and GPT 4o perform well out-of-the-box and do significantly better than previous versions. Llama and Mistral perform poorly but show some promise of improvement with fine tuning.
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2025 1verdicts
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QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines
A fine-tuned ensemble of a generative small language model and an embedding model outperformed zero-shot LLMs on Korean search relevance labeling, with reported Cohen's kappa of 0.646 versus 0.387 and 60x lower latency.