Summary of Knowledge Distillation in Automated Annotation: Supervised Text Classification with Llm-generated Training Labels, by Nicholas Pangakis and Samuel Wolken
Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels
by Nicholas Pangakis, Samuel Wolken
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper explores the potential for using generative large language models (LLMs) as surrogate training data to fine-tune supervised text classifiers in computational social science. The authors introduce a recommended workflow and test this approach by replicating 14 classification tasks, measuring performance, and comparing results with human-annotated labels. The study uses a novel corpus of English-language text classification datasets from recent high-impact CSS articles. The findings indicate that models fine-tuned on LLM-generated labels perform comparably to those fine-tuned with human annotations. This approach can be a fast, efficient, and cost-effective method for building supervised text classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can help label data for text classification tasks in social sciences. Instead of using human-labeling, researchers can use big language models like GPT-4 to generate labels. The study tested this idea by trying it on 14 different tasks and comparing the results with human-generated labels. They used a special dataset from recent articles in top journals. The results show that computers can do just as well as humans at labeling text data. This means researchers can save time, money, and effort by using computer-generated labels. |
Keywords
» Artificial intelligence » Classification » Gpt » Supervised » Text classification
