大模型识别谣言不同来源效能研究

This study aims to address the new challenges faced by online rumor recognition and explore the effectiveness of large models in recognizing different sources of rumors. Constructing domestic and foreign rumor and AI rumor datasets, and testing the rumor source identification ability of four large models under zero sample settings. Research has found that a single large model has low accuracy in identifying rumors and has a clear tendency towards errors. To improve recognition performance, methods such as pre-training, fine-tuning, and ensemble learning are adopted to significantly enhance the performance of the large model. Furthermore, a model collision based ensemble learning method is proposed to improve the effectiveness of rumor source recognition by utilizing multi model feedback. Experimental results show that the ensemble learning framework can integrate the advantages of various models and significantly improve recognition accuracy. This study verifies the potential and improvement direction of large-scale language models in rumor recognition through empirical research, which helps to cope with the current complex online rumor environment and maintain the clarity of cyberspace.