mlsanityData to 14 Oct 2026

Multimodal Item Parameter Estimation using Simulated Response Probabilitie

arXiv 2608.10154 ↗First posted 10 Aug 2026cs.CL cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 26 references that are in the map. First posted on arXiv 10 Aug 2026.

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11 of 26 references.

References · 11

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  4. 2024Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
  5. 2026BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA
  6. 2026Human-Grounded Multimodal Benchmark with 900K-Scale Aggregated Student Response Distributions from Japan's National Assessment of Academic Ability
  7. 2025Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
  8. 2025Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs