探索用于多模态大语言模型视觉复杂性评估的诊断提示方法：亚马逊搜索结果页面案例研究
Exploring Diagnostic Prompting Approach for Multimodal LLM-based Visual Complexity Assessment: A Case Study of Amazon Search Result Pages
AI总结：
本研究通过对比诊断提示与传统提示方法，发现其在提升多模态大语言模型对亚马逊搜索结果页面视觉复杂性评估的可靠性方面有显著改进，但仍有待进一步优化。
摘要：
本研究探讨诊断提示是否能提高多模态大语言模型（MLLM）在评估亚马逊搜索结果页面（SRP）视觉复杂性方面的可靠性。我们使用200个亚马逊SRP页面和人类专家注释，将诊断提示与基于标准格式原理的提示进行比较。诊断提示在预测人类复杂性判断方面显示出显著改进，F1分数从0.031增加到0.297（相对改进+858%），尽管绝对性能仍较为有限（Cohen's κ = 0.071）。决策树显示，模型优先考虑视觉设计元素（徽章混乱：38.6%的重要性），而人类则强调内容相似性，这表明在推理模式上存在部分一致性。失败案例分析揭示了MLLM视觉感知中的持续挑战，特别是在产品相似性和颜色强度评估方面。我们的发现表明，诊断提示代表了向人类对齐的MLLM评估迈出的有希望的初步步骤，尽管需要继续研究和改进提示方法，以在更大的真实数据集上实现可靠的实用部署。
英文摘要：
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 $κ$ = 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
