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.

使用大型语言模型（Llama V1）作为数据生成器，通过在复杂的学科层次结构中对研究提案进行采样，并设计基于关键词的研究提案生成方法，旨在解决数据不平衡问题，提高专家分配的公平性。实验结果表明，使用这种方法生成的研究提案能够有效解决前述问题，并生成高质量的科学文本数据，有助于模型克服数据不平衡问题。