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cs.CL2026
Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning
Trapoom Ukarapol, Pakhapoom Sarapat, Nut Chukamphaeng
Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In con…
cs.CL2026
ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts
Trapoom Ukarapol, Nut Chukamphaeng, Kunat Pipatanakul +1
The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded risks underexplored. In this work,…
cs.CL2024
Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning
Trapoom Ukarapol, Zhicheng Lee, Amy Xin
While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language…