# Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation

arXiv: https://arxiv.org/abs/2310.05318 · HTML: https://mlsanity.com/paper/2310.05318/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection](https://mlsanity.com/paper/2302.13221/) (2023)
- [Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents](https://mlsanity.com/paper/2212.13402/) (2022)
- [Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective](https://mlsanity.com/paper/2306.16893/) (2023)
- [Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions](https://mlsanity.com/paper/2309.13618/) (2023)
- [CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study](https://mlsanity.com/paper/2307.11346/) (2023)
- [Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification](https://mlsanity.com/paper/2109.06661/) (2021)
- [RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training](https://mlsanity.com/paper/2110.07336/) (2021)
- [Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification](https://mlsanity.com/paper/2209.13519/) (2022)
- [Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation](https://mlsanity.com/paper/2309.01717/) (2023)
- [NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis](https://mlsanity.com/paper/2212.13408/) (2022)
- [SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool](https://mlsanity.com/paper/2308.03983/) (2023)
- [Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals](https://mlsanity.com/paper/2203.10922/) (2022)

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)
- [Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model](https://mlsanity.com/paper/2405.19846/) (2024)
- [Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy](https://mlsanity.com/paper/2406.07404/) (2024)
- [Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training](https://mlsanity.com/paper/2501.15108/) (2025)
- [FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies](https://mlsanity.com/paper/2503.20394/) (2025)
- [scReader: Prompting Large Language Models to Interpret scRNA-seq Data](https://mlsanity.com/paper/2412.18156/) (2024)
- [Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training](https://mlsanity.com/paper/2504.19565/) (2025)
- [Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking](https://mlsanity.com/paper/2505.19722/) (2025)
- [Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching](https://mlsanity.com/paper/2604.05866/) (2026)
- [Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM](https://mlsanity.com/paper/2607.28635/) (2026)

## Abstract (arXiv)

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.