Many-shot In-context Learning

Agarwal Rishabh, Singh Avi, Zhang Lei M., Bohnet Bernd, Rosias Luis, Chan Stephanie, Zhang Biao, Anand Ankesh, Abbas Zaheer, Nova Azade, Co-reyes John D., Chu Eric, Behbahani Feryal, Faust Aleksandra, Larochelle Hugo. Arxiv 2024

[Paper]    
Ethics And Bias Few Shot Fine Tuning In Context Learning Pretraining Methods Prompting Reinforcement Learning Training Techniques

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. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance.

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