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

• URL: http://arxiv.org/abs/2412.05137v1
• Date: Fri, 06 Dec 2024 15:51:22 GMT
• Title: Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?
• Authors: Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons, Arian Askari,
• Abstract summary: Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. We present methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines.
• Score: 1.0562108865927007
• License:
• Abstract: 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complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated.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.

Related papers

• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels [75.77877889764073]
  Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. This study explores whether solely utilizing unlabeled data can elicit strong model capabilities. We propose a new paradigm termed zero-to-strong generalization.
  arXiv  Detail & Related papers  (2024-09-19T02:59:44Z)
• TnT-LLM: Text Mining at Scale with Large Language Models [24.731544646232962]
  Large Language Models (LLMs) automate the process of end-to-end label generation and assignment with minimal human effort. We show that TnT-LLM generates more accurate and relevant label when compared against state-of-the-art baselines. We also share our practical experiences and insights on the challenges and opportunities of using LLMs for large-scale text mining in real-world applications.
  arXiv  Detail & Related papers  (2024-03-18T18:45:28Z)
• Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning [2.88268082568407]
  We develop a method to automatically assign survey papers to a taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models.
  arXiv  Detail & Related papers  (2024-02-16T02:21:59Z)
• Prompt Tuned Embedding Classification for Multi-Label Industry Sector Allocation [2.024620791810963]
  This study benchmarks the performance of Prompt Tuning and baselines for multi-label text classification. It is applied to classifying companies into an investment firm's proprietary industry taxonomy. We confirm that the model's performance is consistent across both well-known and less-known companies.
  arXiv  Detail & Related papers  (2023-09-21T13:45:32Z)
• Retrieval-augmented Multi-label Text Classification [20.100081284294973]
  Multi-label text classification is a challenging task in settings of large label sets. Retrieval augmentation aims to improve the sample efficiency of classification models. We evaluate this approach on four datasets from the legal and biomedical domains.
  arXiv  Detail & Related papers  (2023-05-22T14:16:23Z)
• Hierarchical Multi-Label Classification of Scientific Documents [47.293189105900524]
  We introduce a new dataset for hierarchical multi-label text classification of scientific papers called SciHTC. This dataset contains 186,160 papers and 1,233 categories from the ACM CCS tree. Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities.
  arXiv  Detail & Related papers  (2022-11-05T04:12:57Z)
• Adaptive Self-training for Few-shot Neural Sequence Labeling [55.43109437200101]
  We develop techniques to address the label scarcity challenge for neural sequence labeling models. Self-training serves as an effective mechanism to learn from large amounts of unlabeled data. meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
  arXiv  Detail & Related papers  (2020-10-07T22:29:05Z)
• An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels [49.036212158261215]
  Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications. Current state-of-the-art LMTC models employ Label-Wise Attention Networks (LWANs) We show that hierarchical methods based on Probabilistic Label Trees (PLTs) outperform LWANs. We propose a new state-of-the-art method which combines BERT with LWANs.
  arXiv  Detail & Related papers  (2020-10-04T18:55:47Z)
• Octet: Online Catalog Taxonomy Enrichment with Self-Supervision [67.26804972901952]
  We present a self-supervised end-to-end framework, Octet for Online Catalog EnrichmenT. We propose to train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure. Octet enriches an online catalog in production to 2 times larger in the open-world evaluation.
  arXiv  Detail & Related papers  (2020-06-18T04:53:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.