Many-Shot In-Context Learning

Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Luis Rosias; Stephanie Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal Behbahani; Aleksandra Faust; Hugo Larochelle

arXiv · 2404.11018

Many-Shot In-Context Learning

Rishabh Agarwal·Avi Singh·Lei M. Zhang·Bernd Bohnet·Luis Rosias·Stephanie Chan·Biao Zhang·Ankesh Anand·Zaheer Abbas·Azade Nova·John D. Co-Reyes·Eric Chu·Feryal Behbahani·Aleksandra Faust·Hugo Larochelle

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Abstract

Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced and Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases, can learn high-dimensional functions with numerical inputs, and performs comparably to fine-tuning. We also find that inference cost increases linearly in the many-shot regime, and frontier LLMs benefit from many-shot ICL to varying degrees. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance.

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BibTeX RIS

Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal Behbahani, Aleksandra Faust, Hugo Larochelle. 2024-10-17. Many-Shot In-Context Learning. https://arxiv.org/abs/2404.11018

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