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

arXiv:2605.16991 · cs.CL, cs.AI · Submitted 2026-05-16 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-05-16

Updated: 2026-09-02

Terminology

• Transformer — 39× in this paper · explained in 2 episodes
• Fine-Tuning — 16× in this paper · explained in 5 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Engineering — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Decoupled Weight Decay Regularization
• Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
• An Overview of Multi-Task Learning in Deep Neural Networks
• tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation
• Attention Is All You Need
• Characterizing and Avoiding Negative Transfer
• Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
• Gradient Surgery for Multi-Task Learning
• PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering