Universal Self-Adaptive Prompting

• URL: http://arxiv.org/abs/2305.14926v2
• Date: Sat, 21 Oct 2023 02:25:33 GMT
• Title: Universal Self-Adaptive Prompting
• Authors: Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan O. Arik, Tomas Pfister
• Abstract summary: Universal Self-Adaptive Prompting (USP) is an automatic prompt design approach specifically tailored for zero-shot learning. USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types. We evaluate USP with PaLM and PaLM 2 models and demonstrate performances that are considerably stronger than standard zero-shot baselines.
• Score: 60.67460565566514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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