  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
