arXiv：cs.CL· Yupei Guo, Jiajun He, Xiaohan Shi, Tomoki Toda, Zekun Yang, Bowen Wang, Yukinobu Taniguchi·· 3 小时前AI 评分33
用 LLM 生成的解释检测情感改写假新闻
Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News
AI 导读
研究者提出 Gated Cross Attention（GCA）框架，将 LLM 从原始新闻生成的解释作为稳定背景知识，与情感改写后的新闻自适应融合，以提升事实不变、情感改写场景下的假新闻检测鲁棒性。
正文
Abstract:The spread of fake news may cause severe social consequences. Existing fake news detection methods mainly focus on stylistic variations or incorporate external information such as explanations. However, news articles are often rewritten under different emotional backgrounds while preserving their underlying factual claims, which may affect the robustness of detection models. In this work, we investigate fake news detec- tion under fact-preserving emotional variations. To study this problem, we construct emotion-rewritten test sets and generate explanations from the original news articles as stable background knowledge. We then propose a Gated Cross Attention (GCA) framework that adaptively integrates emotionally rewritten news with the corresponding explanations, enabling the model to focus on informative explanation content while reducing potential mismatches caused by emotional reframing. Experiments on PolitiFact, GossipCop, and LUN demonstrate that the proposed method achieves notable improvements under multiple emotional conditions on PolitiFact and LUN, while maintaining competitive performance on GossipCop. We further analyze the effects of explanation guidance and gating mechanisms under different emotional conditions. Our code and data are available at: this https URL gca .
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08835 [cs.CL] |
| (or arXiv:2610.08835v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08835 arXiv-issued DOI via DataCite |
Submission history
From: Zekun Yang [view email]
[v1]
Tue, 29 Sep 2026 11:50:30 UTC (852 KB)
来源：arXiv：cs.CL · arxiv.org