Multimodal Item Parameter Estimation using Simulated Response Probabilitie
arXiv:2608.10154v1 Announce Type: new Abstract: We present results from reconstructing multiple-choice model (MCM) and three-parameter logistic (3PL) model curves using a fine-tuned multimodal large language model (LLM) based on Qwen3.5. The model is prompted and fine-tuned to replicate choice probabilities across a large training corpus of multiple-choice items containing both image and text stimuli, conditioned on a labeled set of student ability levels. By learning to reproduce the systematic error patterns of students across a discrete range of abilities, the LLM implicitly captures the underlying response probabilities encoded in the 3PL and MCM curves. This allows us to accurately approximate item difficulty on a held-out test set directly from the model's predicted option probabilities.
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[Submitted on 10 Aug 2026]
Title:Multimodal Item Parameter Estimation using Simulated Response Probabilitie
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Abstract:We present results from reconstructing multiple-choice model (MCM) and three-parameter logistic (3PL) model curves using a fine-tuned multimodal large language model (LLM) based on Qwen3.5. The model is prompted and fine-tuned to replicate choice probabilities across a large training corpus of multiple-choice items containing both image and text stimuli, conditioned on a labeled set of student ability levels. By learning to reproduce the systematic error patterns of students across a discrete range of abilities, the LLM implicitly captures the underlying response probabilities encoded in the 3PL and MCM curves. This allows us to accurately approximate item difficulty on a held-out test set directly from the model's predicted option probabilities.
Comments: Submitted and Accepted for AIME-Con 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.10154 [cs.CL]
(or arXiv:2608.10154v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.10154
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Christopher Ormerod [view email] [v1] Mon, 10 Aug 2026 19:15:39 UTC (32 KB)
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