Interactive story reading is common in early childhood education, where teachers expect to
teach both language skills and real-world knowledge beyond the story. While many story …

In the context of text classification, the financial burden of annotation exercises for creating
training data is a critical issue. Active learning techniques, particularly those rooted in …

While most existing works on LLM prompting techniques focus only on how to select a better
set of data samples inside one single prompt input (In-Context Learning or ICL), why can not …

Researchers have long recognized the socio-technical gaps in personal tracking research,
where machines can never fully model the complexity of human behavior, making it only …

Domain-specific languages that use a lot of specific terminology often fall into the category of
low-resource languages. Collecting test datasets in a narrow domain is time-consuming and …

Previous research in software application domain classification has faced challenges due to
the lack of a proper taxonomy that explicitly models relations between classes. As a result …

We found that a simple property of clusters in a clustered dataset of news correlate strongly
with importance and urgency of news (IUN) as assessed by LLM. We verified our finding …

This study introduces GPTA, a Large Language Model assistance training framework, that
enhances the training of downstream task models via prefix prompt. By minimizing data …

Collaboration tools contain many intents in workplace conversation, and identifying these
intents is important to increase workplace productivity. However, labelling these intents for a …