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 and can learn high-dimensional functions with numerical inputs. Our
analysis also reveals the limitations of next-token prediction loss as an
indicator of downstream ICL performance.

大型语言模型在少样本上下文学习方面表现出色，通过在推断时从上下文中提供的少量示例进行学习，无需任何权重更新。新的扩展上下文窗口使我们能够研究在上下文中使用数百或数千个示例进行学习的多样本学习方法。从少样本到多样本，我们观察到在各种生成性和判别性任务中取得了显著的性能提升。然而，多样本学习在可用的人类生成示例方面存在瓶颈。为了缓解这一限制，我们探索了两种新的设置：增强和无监督的多样本学习方法。增强多样本学习方法使用模型生成的思考链替代人类示例。无监督多样本学习方法则完全取消了提示信息中的理由，并仅通过领域特定问题对模型进行提示。我们发现，增强和无监督多样本学习方法在多样本学习中非常有效，特别是在复杂推理任务中。最后，我们证明与少样本学习不同，多样本学习可以有效地覆盖预训练偏差，并学习具有数值输入的高维函数。我们的分析还揭示了下一个标记预测损失作为下游上下文学习性能指标的局限性。