Explainable Procedural Mistake Detection

• URL: http://arxiv.org/abs/2412.11927v1
• Date: Mon, 16 Dec 2024 16:13:55 GMT
• Title: Explainable Procedural Mistake Detection
• Authors: Shane Storks, Itamar Bar-Yossef, Yayuan Li, Zheyuan Zhang, Jason J. Corso, Joyce Chai,
• Abstract summary: Procedural mistake detection is a challenging sub-problem of classifying whether a human user has successfully executed the task at hand.<n>We recast PMD to an explanatory self-dialog of questions and answers, which serve as evidence for a decision.<n>Our results show that while open-source VLMs struggle with this task off-the-shelf, their accuracy, coherence, and dialog efficiency can be vastly improved.
• Score: 27.40806437649092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated task guidance has recently attracted attention from the AI research community. Procedural mistake detection (PMD) is a challenging sub-problem of classifying whether a human user (observed through egocentric video) has successfully executed the task at hand (specified by a procedural text). Despite significant efforts in building resources and models for PMD, machine performance remains nonviable, and the reasoning processes underlying this performance are opaque. As such, we recast PMD to an explanatory self-dialog of questions and answers, which serve as evidence for a decision. As this reformulation enables an unprecedented transparency, we leverage a fine-tuned natural language inference (NLI) model to formulate two automated coherence metrics for generated explanations. Our results show that while open-source VLMs struggle with this task off-the-shelf, their accuracy, coherence, and dialog efficiency can be vastly improved by incorporating these coherence metrics into common inference and fine-tuning methods. Furthermore, our multi-faceted metrics can visualize common outcomes at a glance, highlighting areas for improvement.

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