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WangchanLion and WangchanX MRC Eval
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This technical report describes the development of WangchanLion, an instruction fine-tuned model focusing on Machine Reading Comprehension (MRC) in the Thai language. Our model is based on SEA-LION and a collection of instruction following datasets. To promote open research and reproducibility, we publicly release all training data, code, and the final model weights under the Apache-2 license. To assess the contextual understanding capability, we conducted extensive experimental studies using two Thai MRC datasets, XQuAD and Iapp_wiki_qa_squad. Experimental results demonstrate the model's ability to comprehend the context and produce an answer faithful to the reference one in 0-shot and 1-shot settings. In addition, our evaluation goes beyond the traditional MRC. We propose a new evaluation scheme assessing the answer's correctness, helpfulness, conciseness, and contextuality. Our code is available publicly at https://github.com/vistec-AI/WangchanLion.
Forward citations
Cited by 2 Pith papers
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Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation
This paper quantifies Western-centric bias in MMLU, releases Global-MMLU across 42 languages with human-verified translations, and shows model rankings shift on culturally sensitive versus agnostic subsets.
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Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
A seed-free, Wikipedia-backed synthetic data pipeline lets a 5,000-example Thai fine-tune reach BERTScore close to Thai LLMs trained on tens of thousands to hundreds of thousands of instructions.
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