  In addressing the imbalanced issue of data within the realm of Natural
Language Processing, text data augmentation methods have emerged as pivotal
solutions. This data imbalance is prevalent in the research proposals submitted
during the funding application process. Such imbalances, resulting from the
varying popularity of disciplines or the emergence of interdisciplinary
studies, significantly impede the precision of downstream topic models that
deduce the affiliated disciplines of these proposals. At the data level,
proposals penned by experts and scientists are inherently complex technological
texts, replete with intricate terminologies, which augmenting such specialized
text data poses unique challenges. At the system level, this, in turn,
compromises the fairness of AI-assisted reviewer assignment systems, which
raises a spotlight on solving this issue. This study leverages large language
models (Llama V1) as data generators to augment research proposals categorized
within intricate disciplinary hierarchies, aiming to rectify data imbalances
and enhance the equity of expert assignments. We first sample within the
hierarchical structure to find the under-represented class. Then we designed a
prompt for keyword-based research proposal generation. Our experiments attests
to the efficacy of the generated data, demonstrating that research proposals
produced using the prompts can effectively address the aforementioned issues
and generate high quality scientific text data, thus help the model overcome
the imbalanced issue.
