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What Makes In-Context Learning Work?
In recent years, large language models (LMs) have demonstrated remarkable capabilities in performing various tasks through a process known as in-context learning. This approach allows models to condition their predictions on a few input-label pairs, or demonstrations, without requiring explicit training or fine-tuning.
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Self-Generated In-Context Learning
Self-Generated In-Context Learning (SG-ICL) represents a transformative approach in the field of artificial intelligence, particularly in natural language processing. By leveraging pre-trained language models (PLMs) to autonomously generate contextual demonstrations, SG-ICL significantly reduces the dependency on external datasets, allowing AI systems to adapt to new tasks without extensive retraining.
Tag: IN_CONTEXT_LEARNING
AI adapting to tasks using examples in prompts. Explore how models learn on-the-fly without fine-tuning, enhancing versatility.