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Commonsense norms are defeasible by context: reading books is usually great,but not when driving a car. While contexts can be explicitly described inlanguage, in embodied scenarios, contexts are often provided visually. Thistype of visually grounded reasoning about defeasible commonsense norms isgenerally easy for humans, but (as we show) poses a challenge for machines, asit necessitates both visual understanding and reasoning about commonsensenorms. We construct a new multimodal benchmark for studying visual-groundedcommonsense norms: NORMLENS. NORMLENS consists of 10K human judgmentsaccompanied by free-form explanations covering 2K multimodal situations, andserves as a probe to address two questions: (1) to what extent can models alignwith average human judgment? and (2) how well can models explain theirpredicted judgments? We find that state-of-the-art model judgments andexplanations are not well-aligned with human annotation. Additionally, wepresent a new approach to better align models with humans by distilling socialcommonsense knowledge from large language models. The data and code arereleased at https://seungjuhan.me/normlens.

Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms