大型语言模型程序

摘要

近年来，大型预训练语言模型（LLMs）展示了能够遵循指令并从少量示例中执行新任务的能力。通过在上下文示例中对LLM进行参数化的可能性，可以在比微调低得多的成本下扩展它们的能力。我们延伸了这一推理，并提出了一种通过将LLM嵌入算法或程序进一步扩展其能力的方法。为了展示这种方法的好处，我们提出了一个证据支持的问答示例。通过更加算法化的方法，我们在不进行任何微调的情况下比思维链基准获得了6.4\%的改进。此外，我们重点介绍了从这一角度出发的最新工作，并讨论了与标准方法相比的优缺点。

English

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.

大型语言模型程序

Large Language Model Programs

摘要

Support