Papers › WangchanLion and WangchanX MRC Eval
WangchanLion and WangchanX MRC Eval
Wannaphong Phatthiyaphaibun, Surapon Nonesung, Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Jitkapat Sawatphol, Chompakorn Chaksangchaichot, Ekapol Chuangsuwanich, Sarana Nutanong
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.
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