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AI Research
arXiv CS.AI·Jun 15·5 min read
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
TL;DR
A challenge in question classification across different datasets is the inconsistency in model performance. This study developed and evaluated supervised models and prompted large language models (LLMs) for classifying Bloom's taxonomy questions.
✦ Why It Matters
Engineers can leverage prompted LLMs for more accurate question classification in educational applications.
Key Takeaways
- Explore LLMs for educational question classification tasks.
- Utilize in-context examples for better LLM performance.
- Implement a lightweight UI for classifying question banks.
- Evaluate cross-dataset performance of your ML models.
How It Works
The study employs various prompting strategies for LLMs, particularly focusing on combining in-context examples with action verbs specific to the course material. This approach enhances the model's understanding and classification accuracy across different datasets.