WangchanLion and WangchanX MRC Eval

Wannaphong Phatthiyaphaibun, Surapon Nonesung, Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Can Udomcharoenchaikit +4 more
3/24/2024
cs.CLcs.AI

Official code: vistec-AI/WangchanLion (5 stars, last commit 2024-05-29).

Abstract

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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50PythonDec 12, 20232 years ago

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@article{phatthiyaphaibun2024wangchanlion,
  title  = {WangchanLion and WangchanX MRC Eval},
  author = {Wannaphong Phatthiyaphaibun and Surapon Nonesung and Patomporn Payoungkhamdee and Peerat Limkonchotiwat and Can Udomcharoenchaikit and Jitkapat Sawatphol and Chompakorn Chaksangchaichot and Ekapol Chuangsuwanich and Sarana Nutanong},
  year   = {2024},
  eprint = {2403.16127},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.16127v2}
}

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