用大模型替代人工标注，提升概念瓶颈模型的可解释性
If Concept Bottlenecks are the Question, are Foundation Models the Answer?
- 用大模型弱监督替代专家标注生成概念
- 发现大模型生成概念准确率与任务表现不强相关
- 适合关注可解释性与模型可靠性研究者
概念瓶颈模型（CBMs）通过将输入映射到高层概念（如可见物体及其属性），实现高性能与事前可解释性的结合。其性能和可解释性依赖于概念质量，而传统上依赖昂贵的专家标注。近期研究提出使用视觉语言模型（VLM）进行弱监督以替代人工标注。本文对当前最先进的VLM-CBM架构进行实证分析，采用多项关键指标评估其学习概念的质量。结果表明，不同任务下VLM监督生成的概念与专家标注存在显著差异，且概念准确性与任务性能之间无强相关性。代码已公开于https://github.com/debryu/CQA。
原文摘要 · Abstract (English)
Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g., tagging or scoring an image) in an interpretable manner. Their performance and interpretability, however, hinge on the quality of the concepts they learn. The go-to strategy for ensuring good quality concepts is to leverage expert annotations, which are expensive to collect and seldom available in applications. Researchers have recently addressed this issue by introducing "VLM-CBM" architectures that replace manual annotations with weak supervision from foundation models. It is however unclear what is the impact of doing so on the quality of the learned concepts. To answer this question, we put state-of-the-art VLM-CBMs to the test, analyzing their learned concepts empirically using a selection of significant metrics. Our results show that, depending on the task, VLM supervision can sensibly differ from expert annotations, and that concept accuracy and quality are not strongly correlated. Our code is available at https://github.com/debryu/CQA.
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