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 publically 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. Evaluation results
provide insight into how we can improve our model in the future. Our code is
public at this https URL

该研究报告描述了 WangchanLion 的开发，它是一种针对泰语机器阅读理解（MRC）的指令微调模型。通过使用泰语 MRC 数据集进行广泛的实验研究，我们证明了该模型在零次训练和一次训练设置下，能够理解上下文并生成与参考答案一致的回答。此外，我们还提出了一种新的评估方案，评估答案的正确性、帮助性、简洁性和上下文相关性，评估结果为未来改进模型提供了见解。

王岔狮和王岔 X MRC 评估

WangchanLion and WangchanX MRC Eval

In the era of Large Language Models (LLMs), tremendous strides have been made
in the field of multimodal understanding. However, existing advanced algorithms
are limited to effectively utilizing the immense representation capabilities
and rich world knowledge inherent to these large pre-trained models, and the
beneficial connections among tasks within the context of text-rich scenarios
have not been sufficiently explored. In this work, we introduce UniDoc, a novel
multimodal model equipped with text detection and recognition capabilities,
which are deficient in existing approaches. Moreover, UniDoc capitalizes on the
beneficial interactions among tasks to enhance the performance of each
individual task. To implement UniDoc, we perform unified multimodal instruct
tuning on the contributed large-scale instruction following datasets.
Quantitative and qualitative experimental results show that UniDoc sets
state-of-the-art scores across multiple challenging benchmarks. To the best of
our knowledge, this is the first large multimodal model capable of simultaneous
text detection, recognition, spotting, and understanding.

在大型语言模型（LLMs）时代，我们介绍了一种名为 UniDoc 的新型多模态模型，该模型具备文本检测和识别能力，并通过任务之间的有益互动来提升整体性能。利用大规模指令遵循数据集进行统一的多模态指导调优，实验结果表明 UniDoc 在多个挑战性基准测试中取得了最先进的成绩，是第一个能够同时进行文本检测、识别、定位和理解的大型多模态模型。