计算机科学与探索2026，Vol.20Issue(8)：2251-2261,11.DOI:10.3778/j.issn.1673-9418.2512011
协同大语言模型与小语言模型的虚假新闻检测方法
Fake News Detection with Collaborative Large and Small Language Models
摘要
Abstract
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.关键词
虚假新闻检测/大模型/小模型/文本推理/信息挖掘Key words
fake news detection/large language model/small language model/text reasoning/information mining分类
信息技术与安全科学引用本文复制引用
吴联仁,关梓聪..协同大语言模型与小语言模型的虚假新闻检测方法[J].计算机科学与探索,2026,20(8):2251-2261,11.基金项目
国家自然科学基金面上项目(72574048,72274119). This work was supported by the National Natural Science Foundation of China(72574048,72274119). （72574048,72274119）
