研究表明 MLLM 可充当人类替身评估可解释 AI

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.

本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：XAI、MLLM、Qwen、OpenAI，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始信息发布自一手权威信源「arXiv: OpenAI」（Tier-1 一手发布），具备高确定性参考价值。截至目前，全网已有 11 家独立信源对该事件进行交叉印证与跟进报道（含 IT之家、Gorden Sun、Bindu Reddy 等），属于近期高关注度行业信号。