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: real information
mis: misinformation
dis: disinformation
mal: mal-information

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 performance across different labels, indicating that the dataset effectively challenges the verification capabilities of various language models.

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.

⚠️ Usage Disclaimer

This dataset is provided strictly for research purposes only.
Some content in the dataset originates from news sources that may be protected under copyright.
We are not responsible for any legal consequences arising from the commercial use of this dataset.
Use of this dataset for commercial purposes may violate intellectual property rights — proceed at your own risk.

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