The paper presents RoboAbstention, a new benchmark showing frontier VLMs and embodied planners abstain on only 16.5-39% of 6,069 instructions grounded in robotics images, with prompting interventions raising rates to 88-93% but not solving the problem.
Do large language models know what they don’t know? InFindings of the Association for Computational Linguistics, 2023
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The Yes-Man Syndrome: Benchmarking Abstention in Embodied Robotic Agents
The paper presents RoboAbstention, a new benchmark showing frontier VLMs and embodied planners abstain on only 16.5-39% of 6,069 instructions grounded in robotics images, with prompting interventions raising rates to 88-93% but not solving the problem.