Stance detection, the classification of attitudes expressed in a text towards
a specific topic, is vital for applications like fake news detection and
opinion mining. However, the scarcity of labeled data remains a challenge for
this task. To address this problem, we propose Dynamic Model Adaptation with
Contextual Data Generation (DyMoAdapt) that combines Few-Shot Learning and
Large Language Models. In this approach, we aim to fine-tune an existing model
at test time. We achieve this by generating new topic-specific data using
GPT-3. This method could enhance performance by allowing the adaptation of the
model to new topics. However, the results did not increase as we expected.
Furthermore, we introduce the Multi Generated Topic VAST (MGT-VAST) dataset,
which extends VAST using GPT-3. In this dataset, each context is associated
with multiple topics, allowing the model to understand the relationship between
contexts and various potential topics

利用 GPT-3 生成动态模型适应与上下文数据生成的方法（DyMoAdapt）来进行态度检测，提出了多生成主题 VAST（MGT-VAST）数据集，扩展了 VAST，从而帮助模型理解上下文与不同潜在主题之间的关系