Misinformation poses a critical societal challenge, and current approaches
have yet to produce an effective solution. We propose focusing on
generalization, soft classification, and leveraging recent large language
models to create more practical tools in contexts where perfect predictions
remain unattainable. We begin by demonstrating that GPT-4 and other language
models can outperform existing methods in the literature. Next, we explore
their generalization, revealing that GPT-4 and RoBERTa-large exhibit critical
differences in failure modes, which offer potential for significant performance
improvements. Finally, we show that these models can be employed in soft
classification frameworks to better quantify uncertainty. We find that models
with inferior hard classification results can achieve superior soft
classification performance. Overall, this research lays groundwork for future
tools that can drive real-world progress on misinformation.

本文旨在探究应用通用性、软分类及最新大型语言模型等技术解决误导信息带来的挑战。作者通过比较发现，GPT-4 及其他语言模型相比早期方法有更好的表现，且这些模型应用在软分类框架中，可帮助更好地量化不确定性，而这可能带来意义重大的性能提升。此外，作者还发现 GPT-4 和 RoBERTa-large 在易错性方面存在重大差异，这为深入研究本领域提供了重要线索。总之，本研究为未来进一步解决误导信息带来的实际问题奠定了基础。