Skill neologisms are optimized soft tokens that enhance specific LLM skills and support zero-shot composition on synthetic and Skill-Mix tasks.
arXiv preprint arXiv:2007.12061 (2020)
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EduEmbed fine-tunes language models in two stages to add semantic information to learner-item embeddings and improve performance on cognitive diagnosis and adaptive testing tasks.
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
A framework using latent class analysis on student data to define personas, LLM simulations of their responses, and ridge regression improves IRT difficulty prediction for MCQs over baselines.
citing papers explorer
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Skill Neologisms: Towards Skill-based Continual Learning
Skill neologisms are optimized soft tokens that enhance specific LLM skills and support zero-shot composition on synthetic and Skill-Mix tasks.
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Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling
EduEmbed fine-tunes language models in two stages to add semantic information to learner-item embeddings and improve performance on cognitive diagnosis and adaptive testing tasks.
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Graph-Based Alternatives to LLMs for Human Simulation
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
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MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
A framework using latent class analysis on student data to define personas, LLM simulations of their responses, and ridge regression improves IRT difficulty prediction for MCQs over baselines.