Misinformation is prevalent in various fields such as education, politics,
health, etc., causing significant harm to society. However, current methods for
cross-domain misinformation detection rely on time and resources consuming
fine-tuning and complex model structures. With the outstanding performance of
LLMs, many studies have employed them for misinformation detection.
Unfortunately, they focus on in-domain tasks and do not incorporate significant
sentiment and emotion features (which we jointly call affect). In this paper,
we propose RAEmoLLM, the first retrieval augmented (RAG) LLMs framework to
address cross-domain misinformation detection using in-context learning based
on affective information. It accomplishes this by applying an emotion-aware LLM
to construct a retrieval database of affective embeddings. This database is
used by our retrieval module to obtain source-domain samples, which are
subsequently used for the inference module's in-context few-shot learning to
detect target domain misinformation. We evaluate our framework on three
misinformation benchmarks. Results show that RAEmoLLM achieves significant
improvements compared to the zero-shot method on three datasets, with the
highest increases of 20.69%, 23.94%, and 39.11% respectively. This work will be
released on this https URL

该论文提出了 RAEmoLLM 框架，通过应用情感感知 LLM 构建一个含有情感嵌入的检索数据库，以实现跨领域的虚假信息检测。实验结果显示，RAEmoLLM 较零样本方法在三个数据集上均获得显著提高，其中最高分别提高了 20.69%、23.94% 和 39.11%。