In this study, we present a novel and challenging multilabel Vietnamese
dataset (RMDM) designed to assess the performance of large language models
(LLMs), in verifying electronic information related to legal contexts, focusing
on fake news as potential input for electronic evidence. The RMDM dataset
comprises four labels: real, mis, dis, and mal, representing real information,
misinformation, disinformation, and mal-information, respectively. By including
these diverse labels, RMDM captures the complexities of differing fake news
categories and offers insights into the abilities of different language models
to handle various types of information that could be part of electronic
evidence. The dataset consists of a total of 1,556 samples, with 389 samples
for each label. Preliminary tests on the dataset using GPT-based and BERT-based
models reveal variations in the models' performance across different labels,
indicating that the dataset effectively challenges the ability of various
language models to verify the authenticity of such information. Our findings
suggest that verifying electronic information related to legal contexts,
including fake news, remains a difficult problem for language models,
warranting further attention from the research community to advance toward more
reliable AI models for potential legal applications.

本研究提出了一个新的、具有挑战性的多标签越南数据集 (RMDM)，用于评估大型语言模型 (LLMs) 在验证与法律背景相关的电子信息方面的表现，特别关注作为电子证据潜在输入的假新闻。RMDM 数据集包含四个标签：real、mis、dis 和 mal，分别代表真实信息、错误信息、误导信息和恶意信息。通过包含这些不同的标签，RMDM 捕捉了不同假新闻类别的复杂性，并为处理可能成为电子证据一部分的各种信息的不同语言模型的能力提供了见解。该数据集共有 1556 个样本，每个标签有 389 个样本。使用基于 GPT 和基于 BERT 的模型对数据集进行的初步测试显示，不同标签之间模型的性能存在差异，这表明该数据集有效地挑战了各种语言模型验证这种信息真实性的能力。我们的研究结果表明，验证与法律背景相关的电子信息，包括假新闻，对于语言模型仍然是一个困难的问题，需要进一步引起研究界的关注，以推进更可靠的人工智能模型，用于潜在的法律应用。