Despite recent advancements in detecting disinformation generated by large
language models (LLMs), current efforts overlook the ever-evolving nature of
this disinformation. In this work, we investigate a challenging yet practical
research problem of detecting evolving LLM-generated disinformation.
Disinformation evolves constantly through the rapid development of LLMs and
their variants. As a consequence, the detection model faces significant
challenges. First, it is inefficient to train separate models for each
disinformation generator. Second, the performance decreases in scenarios when
evolving LLM-generated disinformation is encountered in sequential order. To
address this problem, we propose DELD (Detecting Evolving LLM-generated
Disinformation), a parameter-efficient approach that jointly leverages the
general fact-checking capabilities of pre-trained language models (PLM) and the
independent disinformation generation characteristics of various LLMs. In
particular, the learned characteristics are concatenated sequentially to
facilitate knowledge accumulation and transformation. DELD addresses the issue
of label scarcity by integrating the semantic embeddings of disinformation with
trainable soft prompts to elicit model-specific knowledge. Our experiments show
that \textit{DELD} significantly outperforms state-of-the-art methods.
Moreover, our method provides critical insights into the unique patterns of
disinformation generation across different LLMs, offering valuable perspectives
in this line of research.

探测不断演化的大型语言模型生成的虚假信息的研究问题，通过预训练语言模型的普遍事实核查能力和各种大型语言模型的独立虚假信息生成特征，提出一种参数高效的方法 DELD，通过序列连接模型的学习特征以促进知识积累和转换，并通过与可训练的软提示结合，解决标签稀缺的问题，实验证明该方法显著优于现有方法，对不同大型语言模型的虚假信息生成模式提供了重要洞察，为该领域研究提供了有价值的观点。