In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to parameterise an LLM through such in-context examples widens their capability at a much lower cost than finetuning. We extend this line of reasoning and present a method which further expands the capabilities of an LLM by embedding it within an algorithm or program. To demonstrate the benefits of this approach, we present an illustrative example of evidence-supported question-answering. We obtain a 6.4\% improvement over the chain of thought baseline through a more algorithmic approach without any finetuning. Furthermore, we highlight recent work from this perspective and discuss the advantages and disadvantages in comparison to the standard approaches.

翻译：近年来，大规模预训练语言模型已展现出从少量示例中遵循指令并执行新任务的能力。通过此类上下文示例对语言模型进行参数化的可能性，使其能够以远低于微调的成本扩展其能力。我们延伸这一推理思路，提出一种将语言模型嵌入算法或程序以进一步拓展其能力的方法。为展示该方法的优势，我们提供一个基于证据支持问答的说明性示例。通过更具算法性的方法，我们在无需任何微调的情况下，相较于思维链基线取得6.4%的性能提升。此外，我们从该视角梳理近期相关工作，并讨论其相较于标准方法的优劣。