Summary of Can Large Language Models Serve As Effective Classifiers For Hierarchical Multi-label Classification Of Scientific Documents at Industrial Scale?, by Seyed Amin Tabatabaei et al.
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?
by Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons, Arian Askari
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution addresses the hierarchical multi-label classification (HMC) task on scientific documents at an industrial scale. It tackles the challenge of classifying hundreds of thousands of documents across thousands of dynamic labels efficiently and scalably. Traditional machine learning approaches are impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have potential in complex tasks like multi-label classification, but applying them to large and dynamic taxonomies presents unique challenges. The approach combines LLMs with dense retrieval techniques to overcome these challenges, leveraging zero-shot HMC for real-time label assignment. Evaluations on the SSRN dataset demonstrate significant improvements in both classification accuracy and cost-efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research solves a big problem: quickly sorting huge numbers of scientific papers into many categories that change over time. Right now, we don’t have good ways to do this efficiently. Large Language Models are great at classifying documents, but they can only handle a certain number of categories. The authors developed new methods that combine these models with other techniques to overcome this limitation. They tested their approach on a massive database of preprints and showed it works much better than before. This research is important because it helps us understand how to use Large Language Models for big tasks like this one. |
Keywords
» Artificial intelligence » Classification » Machine learning » Zero shot
