"The broader spectrum of in-context learning"

In-context learning is just fancy pattern matching that emerges naturally from language processing

This paper expands our understanding of in-context learning beyond few-shot supervised learning, showing how it emerges from basic language processing capabilities.

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https://arxiv.org/abs/2412.03782

🤔 Original Problem:

Research has focused narrowly on few-shot supervised in-context learning, missing the broader spectrum of how LLMs adapt to context through instructions, role-play, and other mechanisms.

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💡 Solution in this Paper:

→ The paper reframes in-context learning as any sequence task where context reduces prediction loss

→ It shows how in-context learning emerges naturally from language model training on sequential dependencies

→ The solution connects basic language capabilities like coreference resolution and parallel structure to more complex adaptation

→ This framework unifies different types of in-context learning under a single theoretical perspective

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🔑 Key Insights:

→ In-context learning exists on a spectrum from simple memory tasks to complex adaptation

→ Basic language processing capabilities form the foundation for more sophisticated in-context learning

→ Different types of in-context learning may share neural circuitry and mechanisms

→ Generalization should be evaluated across multiple dimensions: what is learned, how it's learned, and how it's applied

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📊 Results:

→ Early transformer models like GPT-1/2 showed improved coreference resolution, marking progress in basic in-context learning

→ LLMs demonstrate flexible generalization across domains, from integers to abstract concepts

→ Models can learn from various formats: examples, instructions, explanations, and role prompts