Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning

The paper proposes a response‑free method for estimating difficulty of reading‑comprehension multiple‑choice items by fine‑tuning a transformer on item wording. It introduces two extensions to a baseline joint‑encoding model: a component‑wise variant that encodes passage, question, and options separately, and a multi‑task variant that adds a question‑answering auxiliary task. Experiments on a corpus of nearly 30,000 items show that both extensions outperform the baseline, especially the multi‑task variant across all metrics and the component‑wise variant in rank ordering, even with limited training data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• nlp
• fine-tuning