Detecting fake news requires both a delicate sense of diverse clues and a
profound understanding of the real-world background, which remains challenging
for detectors based on small language models (SLMs) due to their knowledge and
capability limitations. Recent advances in large language models (LLMs) have
shown remarkable performance in various tasks, but whether and how LLMs could
help with fake news detection remains underexplored. In this paper, we
investigate the potential of LLMs in fake news detection. First, we conduct an
empirical study and find that a sophisticated LLM such as GPT 3.5 could
generally expose fake news and provide desirable multi-perspective rationales
but still underperforms the basic SLM, fine-tuned BERT. Our subsequent analysis
attributes such a gap to the LLM's inability to select and integrate rationales
properly to conclude. Based on these findings, we propose that current LLMs may
not substitute fine-tuned SLMs in fake news detection but can be a good advisor
for SLMs by providing multi-perspective instructive rationales. To instantiate
this proposal, we design an adaptive rationale guidance network for fake news
detection (ARG), in which SLMs selectively acquire insights on news analysis
from the LLMs' rationales. We further derive a rationale-free version of ARG by
distillation, namely ARG-D, which services cost-sensitive scenarios without
inquiring LLMs. Experiments on two real-world datasets demonstrate that ARG and
ARG-D outperform three types of baseline methods, including SLM-based,
LLM-based, and combinations of small and large language models.

基于大型语言模型的虚假新闻检测研究发现，大型语言模型虽然能提供多元的合理解释，却无法像基于小语言模型的训练过程那样适当选择和结合解释来得出结论。因此，当前的大型语言模型不能替代小型语言模型在虚假新闻检测中的作用，但可以作为小型语言模型的顾问，提供多元的启示性解释。为了实现这一点，作者设计了自适应启示指导网络（ARG）用于虚假新闻检测，在这个网络中，小型语言模型从大型语言模型的解释中选择性地获取新闻分析的见解。另外，作者还提出了一个消除解释的版本 ARG-D，用于成本敏感的情境，无需查询大型语言模型。实验证明，ARG 和 ARG-D 在两个真实数据集上的表现优于基于小型语言模型、大型语言模型以及小型和大型语言模型组合的三种基准方法。