# Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification

arXiv: https://arxiv.org/abs/2411.01841 · HTML: https://mlsanity.com/paper/2411.01841/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 61 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [Llemma: An Open Language Model For Mathematics](https://mlsanity.com/paper/2310.10631/) (2023)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Text Classification via Large Language Models](https://mlsanity.com/paper/2305.08377/) (2023)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [Image-to-Markup Generation with Coarse-to-Fine Attention](https://mlsanity.com/paper/1609.04938/) (2016)
- [MathBERT: A Pre-Trained Model for Mathematical Formula Understanding](https://mlsanity.com/paper/2105.00377/) (2021)
- [MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education](https://mlsanity.com/paper/2106.07340/) (2021)
- [Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives](https://mlsanity.com/paper/2201.11736/) (2022)
- [DECAF: Deep Extreme Classification with Label Features](https://mlsanity.com/paper/2108.00368/) (2021)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model](https://mlsanity.com/paper/2403.07581/) (2024)
- [Label Supervised LLaMA Finetuning](https://mlsanity.com/paper/2310.01208/) (2023)
- [Label Confusion Learning to Enhance Text Classification Models](https://mlsanity.com/paper/2012.04987/) (2020)
- [JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding](https://mlsanity.com/paper/2206.06315/) (2022)
- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training](https://mlsanity.com/paper/2301.07558/) (2023)
- [Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification](https://mlsanity.com/paper/2310.05128/) (2023)
- [Automatic tagging of knowledge points for K12 math problems](https://mlsanity.com/paper/2208.09867/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback](https://mlsanity.com/paper/2510.22559/) (2025)
- [TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis](https://mlsanity.com/paper/2510.23062/) (2025)
- [Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Training](https://mlsanity.com/paper/2510.25042/) (2025)
- [Mask-Robust Face Verification for Online Learning via YOLOv5 and Residual Networks](https://mlsanity.com/paper/2510.25184/) (2025)

## Abstract (arXiv)

Accurate annotation of educational resources is crucial for effective personalized learning and resource recommendation in online education. However, fine-grained knowledge labels often overlap or share similarities, making it difficult for existing multi-label classification methods to differentiate them. The label distribution imbalance due to sparsity of human annotations further intensifies these challenges. To address these issues, this paper introduces RR2QC, a novel Retrieval Reranking method to multi-label Question Classification by leveraging label semantics and meta-label refinement. First, RR2QC improves the pre-training strategy by utilizing semantic relationships within and across label groups. Second, it introduces a class center learning task to align questions with label semantics during downstream training. Finally, this method decomposes labels into meta-labels and uses a meta-label classifier to rerank the retrieved label sequences. In doing so, RR2QC enhances the understanding and prediction capability of long-tail labels by learning from meta-labels that frequently appear in other labels. Additionally, a mathematical LLM is used to generate solutions for questions, extracting latent information to further refine the model's insights. Experimental results show that RR2QC outperforms existing methods in Precision@K and F1 scores across multiple educational datasets, demonstrating its effectiveness for online education applications. The code and datasets are available at https://github.com/78Erii/RR2QC.