Fact-checking is essential due to the explosion of misinformation in the
media ecosystem. Although false information exists in every language and
country, most research to solve the problem mainly concentrated on huge
communities like English and Chinese. Low-resource languages like Vietnamese
are necessary to explore corpora and models for fact verification. To bridge
this gap, we construct ViWikiFC, the first manual annotated open-domain corpus
for Vietnamese Wikipedia Fact Checking more than 20K claims generated by
converting evidence sentences extracted from Wikipedia articles. We analyze our
corpus through many linguistic aspects, from the new dependency rate, the new
n-gram rate, and the new word rate. We conducted various experiments for
Vietnamese fact-checking, including evidence retrieval and verdict prediction.
BM25 and InfoXLM (Large) achieved the best results in two tasks, with BM25
achieving an accuracy of 88.30% for SUPPORTS, 86.93% for REFUTES, and only
56.67% for the NEI label in the evidence retrieval task, InfoXLM (Large)
achieved an F1 score of 86.51%. Furthermore, we also conducted a pipeline
approach, which only achieved a strict accuracy of 67.00% when using InfoXLM
(Large) and BM25. These results demonstrate that our dataset is challenging for
the Vietnamese language model in fact-checking tasks.

通过构建 ViWikiFC，我们首次为越南维基百科建立了一个手动注释的开放领域语料库，其中收录了超过 20K 个索证句，经由从维基百科文章提取的证据句生成。我们分析了我们的语料库的各个语言方面，包括新的依赖率、新的 n-gram 率和新的词汇率。我们进行了各种越南事实核查的实验，包括证据检索和判断预测。BM25 和 InfoXLM（Large）在两个任务中取得了最佳结果，其中 BM25 在证据检索任务中的 SUPPORTS 准确率为 88.30%，REFUTES 准确率为 86.93%，NEI 标签仅为 56.67%；InfoXLM（Large）在检索任务中取得了 86.51% 的 F1 分数。此外，我们还进行了一种流水线方法，但只有在使用 InfoXLM（Large）和 BM25 时，严格准确率达到了 67.00%。这些结果证明了我们的数据集对于越南语言模型在事实核查任务中是具有挑战性的。

ViWikiFC: 基于越南维基百科的文本知识事实核查源

ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge  Source

The proliferation of fake news has had far-reaching implications on politics,
the economy, and society at large. While Fake news detection methods have been
employed to mitigate this issue, they primarily depend on two essential
elements: the quality and relevance of the evidence, and the effectiveness of
the verdict prediction mechanism. Traditional methods, which often source
information from static repositories like Wikipedia, are limited by outdated or
incomplete data, particularly for emerging or rare claims. Large Language
Models (LLMs), known for their remarkable reasoning and generative
capabilities, introduce a new frontier for fake news detection. However, like
traditional methods, LLM-based solutions also grapple with the limitations of
stale and long-tail knowledge. Additionally, retrieval-enhanced LLMs frequently
struggle with issues such as low-quality evidence retrieval and context length
constraints. To address these challenges, we introduce a novel,
retrieval-augmented LLMs framework--the first of its kind to automatically and
strategically extract key evidence from web sources for claim verification.
Employing a multi-round retrieval strategy, our framework ensures the
acquisition of sufficient, relevant evidence, thereby enhancing performance.
Comprehensive experiments across three real-world datasets validate the
framework's superiority over existing methods. Importantly, our model not only
delivers accurate verdicts but also offers human-readable explanations to
improve result interpretability.

通过多轮检索策略从网络源自动抽取关键证据进行主张验证的检索增强 LLMs 框架是第一种能自动地并有目的性地从网页信息中提取关键证据的框架，通过在三个现实世界数据集上进行全面的实验证明了该框架对现有方法的优越性。重要的是，我们的模型不仅提供准确的结论，还提供可读的解释，以提高结果的可解释性。