RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification
This addresses the problem of verifying fake news as electronic evidence in legal applications for AI researchers, but it is incremental as it primarily provides a new dataset.
The authors introduced RMDM, a multilabel Vietnamese dataset with 1,556 samples across four fake news categories, to evaluate large language models for verifying electronic information in legal contexts, finding that models like GPT and BERT show varied performance, indicating the difficulty of this task.
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.