Alapfogalmak
DEEP-ICL introduces a novel methodology that emphasizes the importance of task definitions in achieving efficient few-shot learning, surpassing traditional ICL limitations.
Kivonat
DEEP-ICL challenges the notion that model size drives in-context learning capabilities by focusing on task definitions. It combines two 3B models to achieve comparable performance to larger models. The framework overcomes pretraining sequence length limitations and supports unlimited demonstrations. DEEP-ICL presents a novel alternative for efficient few-shot learning beyond conventional ICL.
Statisztikák
LLMs - Large Language Models have remarkable capabilities for ICL.
GPT-3 - Demonstrates capability to process tasks based on context.
T5 - Expert base model used in experiments.
LoRA - Low-rank adaptation technique utilized in training.
SuperNI - Dataset used for evaluation with 117 subtasks.
Idézetek
"Improvement from ICL does not directly rely on model size, but essentially stems from understanding task definitions and task-guided learning."
"Our experiments show that even with continual training on just five samples, our ensembling methods outperform both traditional ICL and non-ensembling approaches."
"Our contributions delineate the roles between two models: task definition and task processing, confirming that the primary challenge of ICL is the extraction of task definitions."