Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generalization. We propose UPRISE (Universal Prompt Retrieval for Improving zero-Shot Evaluation), which tunes a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input. Specifically, we demonstrate universality in a cross-task and cross-model scenario: the retriever is tuned on a diverse set of tasks, but tested on unseen task types; we use a small frozen LLM, GPT-Neo-2.7B, for tuning the retriever, but test the retriever on different LLMs of much larger scales, such as BLOOM-7.1B, OPT-66B and GPT3-175B. Additionally, we show that UPRISE mitigates the hallucination problem in our experiments with ChatGPT, suggesting its potential to improve even the strongest LLMs. Our model and code are available at https://github.com/microsoft/LMOps.
@article{arxiv.2303.08518,
title = {UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation},
author = {Daixuan Cheng and Shaohan Huang and Junyu Bi and Yuefeng Zhan and Jianfeng Liu and Yujing Wang and Hao Sun and Furu Wei and Denvy Deng and Qi Zhang},
journal= {arXiv preprint arXiv:2303.08518},
year = {2023}
}