Summary of Enhancing Text Classification Through Llm-driven Active Learning and Human Annotation, by Hamidreza Rouzegar et al.
Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation
by Hamidreza Rouzegar, Masoud Makrehchi
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel active learning framework that integrates human annotators and Large Language Models (LLMs) for efficient text classification. The approach leverages uncertainty sampling to identify the most informative samples for manual annotation, combining it with LLMs like GPT-3.5 for automated annotation. Evaluations on three public datasets, including IMDB, Fake News, and Movie Genres, demonstrate the framework’s effectiveness in balancing cost efficiency and model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to reduce the financial burden of annotating data for text classification tasks. By combining human annotators with LLMs like GPT-3.5, the approach can pinpoint the most instructive samples for manual annotation while also providing an alternative for automated annotation. The framework achieves this by integrating human annotation with the output of LLMs based on their uncertainty levels. |
Keywords
» Artificial intelligence » Active learning » Gpt » Text classification
