通用自适应提示
Universal Self-Adaptive Prompting
AI总结：
提出通用自适应提示（USP），通过少量未标记数据和推理模型自动生成伪示例，将上下文学习推广至零样本设置，在40多个任务中性能优于零样本基线且媲美少样本基线。
摘要：
现代大型语言模型（LLMs）的一个显著特征是其令人印象深刻的通用零样本和少样本能力，这种能力通常通过提示进行上下文学习（ICL）来激发。然而，尽管零样本性能备受期待且最为通用，但由于缺乏指导，以及在缺乏真实标签时难以在通用任务中应用现有的自动提示设计方法，LLMs的零样本性能通常仍然较弱。在本研究中，我们通过提出通用自适应提示（USP）来解决这一问题，这是一种专门为零样本学习（同时兼容少样本）量身定制的自动提示设计方法。USP仅需少量未标记数据和仅用于推理的LLM，具有高度的通用性：为实现通用提示，USP将可能的NLP任务归类为三种可能的任务类型之一，然后使用相应的选择器选择最合适的查询和零样本模型生成的响应作为伪示例，从而以完全自动化的方式将ICL推广到零样本设置中。我们使用PaLM和PaLM 2模型评估了USP，结果表明其性能明显强于标准零样本基线，并且在超过40个自然语言理解、自然语言生成和推理任务中，通常与少样本基线相当甚至更优。
英文摘要：
A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-Adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot). Requiring only a small amount of unlabeled data and an inference-only LLM, USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types and then uses a corresponding selector to select the most suitable queries and zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a fully automated way. We evaluate USP with PaLM and PaLM 2 models and demonstrate performances that are considerably stronger than standard zero-shot baselines and often comparable to or even superior to few-shot baselines across more than 40 natural language understanding, natural language generation, and reasoning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
