mlsanityData to 14 Oct 2026

Visual Error Patterns in Multi-Modal AI: A Statistical Approach

arXiv 2412.00083 ↗First posted 27 Nov 2024cs.LG cs.AI cs.CVcited by 0

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

Abstract (arXiv)

Multi-modal large language models (MLLMs), such as GPT-4o, excel at integrating text and visual data but face systematic challenges when interpreting ambiguous or incomplete visual stimuli. This study leverages statistical modeling to analyze the factors driving these errors, using a dataset of geometric stimuli characterized by features like 3D, rotation, and missing face/side. We applied parametric methods, non-parametric methods, and ensemble techniques to predict classification errors, with the non-linear gradient boosting model achieving the highest performance (AUC=0.85) during cross-validation. Feature importance analysis highlighted difficulties in depth perception and reconstructing incomplete structures as key contributors to misclassification. These findings demonstrate the effectiveness of statistical approaches for uncovering limitations in MLLMs and offer actionable insights for enhancing model architectures by integrating contextual reasoning mechanisms.

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 7 of its 14 references that are in the map. First posted on arXiv 27 Nov 2024.

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7 of 14 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks2 Aug
  2. 2024MM-LLMs: Recent Advances in MultiModal Large Language Models24 Jan

Other references · 5

  1. 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
  2. 2024InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planningcited by 31
  3. 2024LLM-Select: Feature Selection with Large Language Modelscited by 30
  4. 2023Feature Importance versus Feature Influence and What It Signifies for Explainable AIcited by 3
  5. 2024HULLMI: Human vs LLM identification with explainabilitycited by 3

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  1. 2026MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models
  2. 2026AMVICC: A Novel Benchmark for Cross-Modal Failure Mode Profiling for VLMs and IGMs
  3. 2024A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
  4. 2025Multimodal LLM Augmented Reasoning for Interpretable Visual Perception Analysis
  5. 2025The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
  6. 2025Rethinking the Text-Vision Reasoning Imbalance in MLLMs through the Lens of Training Recipes
  7. 2026Cross-Modal Attention Analysis and Optimization in Vision-Language Models: A Study on Visual Reliability
  8. 2025Revisiting Visual Understanding in Multimodal Reasoning through a Lens of Image Perturbation