MAPLE uses graph-influence scores to select and pseudo-label the most useful unlabeled examples, then adaptively chooses demonstrations per query, improving many-shot in-context learning with few human labels.
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MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
MAPLE uses graph-influence scores to select and pseudo-label the most useful unlabeled examples, then adaptively chooses demonstrations per query, improving many-shot in-context learning with few human labels.