Abstract
The use of Transfer Learning & Transformers has steadily improved accuracy and has significantly contributed in solving complex computation problems. However, this transformer led accuracy improvement in Applied AI Analytics specifically in sentiment analytics comes with the dark side. It is observed during experiments that a lot of these improvements in transformer led accuracy of one class of sentiment has been at the cost of polarization of another class of sentiment and the failing of neutrality. This lack of neutrality poses an acute problem in the Applied NLP space, which relies heavily on the computational outputs of sentiment analytics for reliable industry ready tasks.
Abstract (translated)
使用迁移学习和Transformer技术稳步提高了准确性，并在解决复杂计算问题方面做出了重大贡献。然而，在应用人工智能分析，特别是在情感分析中，基于Transformer的准确率提升也带来了一个阴暗面。实验观察发现，一个类别的情感准确率提高往往是以另一个类别的情感极化为代价，导致中立性的缺失。这种缺乏中立性在依赖情感分析计算输出以完成可靠行业任务的应用自然语言处理领域造成了严重问题。
URL
https://arxiv.org/abs/2601.15509
