In-context learning approaches differ fundamentally from supervised learning by necessitating the identification of the task from context before execution during testing, revealing a need to understand when models transition between learning regimes.
Results show that attention over the full context isn’t always critical for task execution, as models hit an effective performance plateau at different points during their layers, with specific layers being more beneficial for task recognition.
#in-context-learning #machine-translation #model-architecture #natural-language-processing #attention-mechanisms
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