区分行为与表征的系统性，揭示当前模型评估的盲区
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey
- 区分行为系统性与表征系统性，提出更深层评估框架
- 分析语言与视觉领域主流基准对行为系统性的测试程度
- 建议结合可解释性方法评估模型内部表征的系统性
系统性是自然语言和视觉任务中组合性的重要属性，有助于模型在新情境下实现强泛化。近年来大量研究提出了评估系统性泛化的基准，并设计了相应模型与训练策略，多以弗多尔与皮利申的挑战为背景。然而，这些工作主要关注行为层面的系统性，而忽视了表征层面的系统性。本文强调这一区分的重要性，并基于哈德利（1994）的分类体系，分析了语言与视觉领域关键基准在测试行为系统性方面的覆盖程度。同时指出，机制可解释性领域的实践为评估模型内部表征的系统性提供了可行路径。
原文摘要 · Abstract (English)
A core aspect of compositionality, systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. This has led to numerous studies proposing benchmarks to assess systematic generalization, as well as models and training regimes designed to enhance it. Many of these efforts are framed as addressing the challenge posed by Fodor and Pylyshyn. However, while they argue for systematicity of representations, existing benchmarks and models primarily focus on the systematicity of behaviour. We emphasize the crucial nature of this distinction. Furthermore, building on Hadley's (1994) taxonomy of systematic generalization, we analyze the extent to which behavioural systematicity is tested by key benchmarks in the literature across language and vision. Finally, we highlight ways of assessing systematicity of representations in ML models as practiced in the field of mechanistic interpretability.
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