Item Difficulty Modeling Using Fine-tuned Small and Large Language Models

This study investigates methods for item difficulty modeling in large-scale assessments using both small and large language models (LLMs). We introduce novel data augmentation strategies, including augmentation on the fly and distribution balancing, that surpass benchmark performances, demonstrating their effectiveness in mitigating data imbalance and improving model performance. Our results showed that fine-tuned small language models (SLMs) such as Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa yielded lower root mean squared error than the first-place model in the BEA 2024 Shared Task competition, whereas domain-specific models like BioClinicalBERT and PubMedBERT did not provide significant improvements due to distributional gaps. Majority voting among SLMs enhanced prediction accuracy, reinforcing the benefits of ensemble learning. LLMs, such as GPT-4, exhibited strong generalization capabilities but struggled with item difficulty prediction, likely due to limited training data and the absence of explicit difficulty-related context. Chain-of-thought prompting and rationale generation approaches were explored but did not yield substantial improvements, suggesting that additional training data or more sophisticated reasoning techniques may be necessary. Embedding-based methods, particularly using NV-Embed-v2, showed promise but did not outperform our best augmentation strategies, indicating that capturing nuanced difficulty-related features remains a challenge.