Unverified50% confidenceFactExact time
Zero-shot evaluation means the model is tested directly for its generalization ability without fine-tuning on the ALFRED training set.
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50%
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Long-term
Relevance
10/30/2026
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Related Claims
UnverifiedZero-shot generalization refers to a model transferring directly to a new dataset without any training or fine-tuning on the target dataset while still maintaining usable performance.74% similarVerifiedTeams training label-based zero-shot models should actively shuffle candidate order during evaluation as a routine robustness check.74% similarUnverifiedZero-shot models rely on visual-semantic associations learned during pre-training, and may make misjudgments when distractors similar to the training distribution appear in the scene.72% similarUnverifiedSince the metric depends only on the weight matrix, it can provide a model evaluation pathway for scenarios where training data is inaccessible.72% similarVerifiedOne-shot learning is not the same as zero-shot learning; the single demonstration relies on prior knowledge accumulated by the model from massive pretraining data.72% similar
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