Abstract
E-commerce live streaming in China, particularly on platforms like Douyin, has become a major sales channel, but hosts often use morphs to evade scrutiny and engage in false advertising. This study introduces the Live Auditory Morph Resolution (LiveAMR) task to detect such violations. Unlike previous morph research focused on text-based evasion in social media and underground industries, LiveAMR targets pronunciation-based evasion in health and medical live streams. We constructed the first LiveAMR dataset with 86,790 samples and developed a method to transform the task into a text-to-text generation problem. By leveraging large language models (LLMs) to generate additional training data, we improved performance and demonstrated that morph resolution significantly enhances live streaming regulation.
Abstract (translated)
在中国，特别是在像抖音这样的平台上，电商直播已成为一个重要的销售渠道。然而，主播们经常使用变音（或称“换声音”）来逃避监管并进行虚假宣传。这项研究引入了“Live Auditory Morph Resolution (LiveAMR)”任务，旨在检测此类违规行为。与之前主要关注社交媒体和地下行业中的文本规避的形态变化研究不同，LiveAMR专注于健康和医疗直播中基于发音的规避。 为了开展这一研究，我们构建了第一个LiveAMR数据集，其中包括86,790个样本，并开发了一种方法将任务转化为一个从语音到文本生成的问题。通过利用大型语言模型（LLMs）来生成额外的训练数据，我们的性能得到了显著提升，并证明形态分析在很大程度上增强了对直播监管的有效性。 简而言之，这项研究通过对健康和医疗领域的电商直播进行发音识别，开发了一种新的检测虚假宣传的方法，并通过大规模的语言模型辅助技术提升了该方法的效果。
URL
https://arxiv.org/abs/2512.23280
