# Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?

arXiv: https://arxiv.org/abs/2412.05137 · HTML: https://mlsanity.com/paper/2412.05137/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Multi-Label Text Classification using Attention-based Graph Neural Network](https://mlsanity.com/paper/2003.11644/) (2020)
- [Hierarchical Multi-Label Classification of Scientific Documents](https://mlsanity.com/paper/2211.02810/) (2022)
- [Recent Advances in Hierarchical Multi-label Text Classification: A Survey](https://mlsanity.com/paper/2307.16265/) (2023)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [Hierarchical Multi-Label Classification of Online Vaccine Concerns](https://mlsanity.com/paper/2402.01783/) (2024)

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

- [Large Language Models Do Multi-Label Classification Differently](https://mlsanity.com/paper/2505.17510/) (2025)
- [MultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents](https://mlsanity.com/paper/2604.12352/) (2026)
- [Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models](https://mlsanity.com/paper/2510.07067/) (2025)
- [KScope: A Framework for Characterizing the Knowledge Status of Language Models](https://mlsanity.com/paper/2506.07458/) (2025)
- [HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy](https://mlsanity.com/paper/2604.07937/) (2026)
- [From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification](https://mlsanity.com/paper/2609.01564/) (2026)
- [LogiPart: Local Large Language Models for Data Exploration at Scale with Logical Partitioning](https://mlsanity.com/paper/2509.22211/) (2025)
- [Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs](https://mlsanity.com/paper/2602.15436/) (2026)
- [AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics](https://mlsanity.com/paper/2604.02156/) (2026)
- [Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces](https://mlsanity.com/paper/2606.06840/) (2026)
- [Positional task conditioning for scalable defect detection across product families in large product catalogs](https://mlsanity.com/paper/2609.09567/) (2026)

## Abstract (arXiv)

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale.