新闻
A Dual-Dimensional LLM Framework for Automated Item Incidental Content Similarity Analysis in Large-Scale Assessments
arXiv cs.AI · 发布于 · 阅读约3分钟
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- 发生了什么
- Researchers developed an LLM framework to detect unintended repetition in test items by analyzing both structure and semantic meaning simultaneously.
- 为何重要
- Assessment engineers building large-scale tests or automated item generators need this to prevent construct-irrelevant redundancy that skews results.
- 注意
- The framework is validated on psychometric indicators and adaptive testing simulations, but real-world deployment effectiveness across diverse assessment contexts remains unproven.
收听本摘要
- llm
- language model
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