Abstract: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 this https URL .
| Comments: | To be published in Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.14257 [cs.CL] |
| (or arXiv:2605.14257v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14257 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Adam Nohejl [view email]
[v1]
Thu, 14 May 2026 01:57:35 UTC (60 KB)