Exploring Diagnostic Prompting Approach for Multimodal LLM-based Visual Complexity Assessment: A Case Study of Amazon Search Result Pages

TL;DR

This paper explores how diagnostic prompting can enhance Multimodal Large Language Models' ability to assess visual complexity in Amazon search pages, showing significant but limited improvements over standard methods.

Contribution

It introduces diagnostic prompting for visual complexity assessment in MLLMs and compares its effectiveness against gestalt principles-based prompting using real-world data.

Findings

Diagnostic prompting improved F1-score by 858%

Models focus on visual design elements, humans on content similarity

Persistent challenges in visual perception for MLLMs

Abstract

This study investigates whether diagnostic prompting can improve Multimodal Large Language Model (MLLM) reliability for visual complexity assessment of Amazon Search Results Pages (SRP). We compare diagnostic prompting with standard gestalt principles-based prompting using 200 Amazon SRP pages and human expert annotations. Diagnostic prompting showed notable improvements in predicting human complexity judgments, with F1-score increasing from 0.031 to 0.297 (+858\% relative improvement), though absolute performance remains modest (Cohen's $\kappa$ = 0.071). The decision tree revealed that models prioritize visual design elements (badge clutter: 38.6\% importance) while humans emphasize content similarity, suggesting partial alignment in reasoning patterns. Failure case analysis reveals persistent challenges in MLLM visual perception, particularly for product similarity and color intensity assessment. Our findings indicate that diagnostic prompting represents a promising initial step toward human-aligned MLLM-based evaluation, though failure cases with consistent human-MLLM disagreement require continued research and refinement in prompting approaches with larger ground truth datasets for reliable practical deployment.