用大模型预测词汇难度，兼顾高准确率与可解释性。
Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?
- 用软标签微调LLM提升评分准确率
- 可解释模型相关系数超0.77
- 发现拼写和题目设计影响难度
我们提出了两种词汇难度预测模型：一种是高精度黑盒模型，在公开赛道取得第一名，通过软标签损失函数微调LLM，相关系数r > 0.91；另一种是可解释模型，优于微调编码器基线，相关系数r > 0.77。进一步分析显示，英国文化协会知识型词汇表（KVL）中词汇难度常受拼写复杂度或题目构造影响，而不仅是词汇本身产出难度。代码已开源：https://github.com/ynklab/vocabulary-difficulty。
原文摘要 · Abstract (English)
We describe two types of models for vocabulary difficulty prediction: a high-accuracy black-box model, which achieved the top shared task result in the open track, and an explainable model, which outperforms a fine-tuned encoder baseline. As the black-box model, we fine-tuned an LLM using a soft-target loss function for effective application to the rating task, achieving r > 0.91. The explainable model provides insights into what impacts the difficulty of each item while maintaining a strong correlation (r > 0.77). We further analyze the results, demonstrating that the difficulty of items in the British Council's Knowledge-based Vocabulary Lists (KVL) is often affected by spelling difficulty or the construction of the test items, in addition to the genuine production difficulty of the words. We make our code available online at https://github.com/ynklab/vocabulary-difficulty .
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