研究表明 MLLM 可充当人类替身评估可解释 AI
Beyond Proxy Metrics: MLLMs as Human Surrogates for Explainable AI Evaluation
做 XAI 研究的朋友可以看这篇：作者测了 14 个多模态模型，发现 OpenAI 和 Qwen 对齐人类判断最好，用它跑解释质量评估能省下真人实验的钱和时间。论文还给了可复用的评估框架和指标定义，不用从零设计。
一篇 arXiv 论文（编号 2611.13926）探讨能否用多模态大语言模型（MLLM）替代人类受试者来自动化评估可解释 AI（XAI）技术。现有评估依赖两类路径：代理指标难以反映解释的实际效用，真人评估虽是黄金标准但成本高、难扩展、不易复现。
作者提出一个"purpose-grounded"评估框架，指标可同时适用于 MLLM 与人类用户，并给出何时可信这类模型来自动化评估的概念性论证。实证测试覆盖五个应用方向（含模型改进）和十四个主流 MLLM。
结果显示所有模型呈正向趋势，其中 OpenAI 和 Qwen 系列与人类判断的对齐度普遍更高，是实际使用时的推荐选择。论文定位是补充而非取代用户测试，目标是让 XAI 研究减少对代理指标的依赖。
Beyond Proxy Metrics: MLLMs as Human Surrogates for Explainable AI Evaluation
Explainable AI (XAI) has expanded into a broad and impactful research area, but the fundamental question of how best to evaluate explanation techniques has proven frustratingly elusive. Methodologies typically revolve around either proxy metrics which tell us little about the utility of an explanation, or human evaluation which---while generally considered the gold standard---is expensive, difficult to scale, and challenging to reproduce. In this paper, we investigate if Multimodal Large Language Models (MLLMs) can act as surrogate human participants to automate the evaluation of XAI. To do this, we first propose a ``purpose-grounded'' evaluation framework with metrics applicable to both MLLMs and human users alike. Then, we offer some conceptual insights as to why and when we can trust these models to automate this process. Lastly, we conduct extensive empirical testing across five purpose-grounded areas such as model improvement and fourteen popular MLLMs. Results indicate that all models trend positively, but OpenAI and Qwen models generally show higher alignment compared to other variants, which would be the recommended choices in practice. We expect our insights not to replace user testing, but rather to help XAI researchers complement their current evaluation methodologies and move the field away from heavy reliance on proxy metrics for measuring explanation utility.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include XAI, MLLM, Qwen, OpenAI.
Primary source verification: published directly by arXiv: OpenAI with first-party authority. Cross-source corroboration: 11 independent sources have covered this development (including IT之家, Gorden Sun, Bindu Reddy), confirming industry-wide signal strength.
