协同大语言模型与小语言模型的虚假新闻检测方法OA

The detection of fake news is critical to mitigating the spread of misinformation.A primary challenge lies in extracting supplementary information that supports the assessment of news authenticity from inherently limited textual content.Traditional approaches are constrained by their limited capacity for deep textual reasoning and the effective integration of multi-dimensional contextual data,making it difficult to leverage external information related to the news content.This paper proposes a novel text-based fake news detection method that combines large language models with fine-tuned small language models.The large language model performs multi-level analysis and semantic mining on both the news content and associated comments,generating enriched analytical features.These features,along with the original news content,are then fed into a compact,task-specific small language model,which excels in classification performance due to its spe-cialization and efficiency.By delegating complex reasoning and information extraction to the large language model and utilizing the small language model as a precise classifier,the proposed framework achieves a synergistic effect that enhances overall detection accuracy.Experimental results demonstrate that this collaborative approach achieves an accuracy of 95.18%on real-world datasets,offering a highly accurate and practically viable solution for detecting fake news in plaintext.