UPRISE: Universal Prompt Retrieval For Improving Zero-shot Evaluation

Cheng Daixuan, Huang Shaohan, Bi Junyu, Zhan Yuefeng, Liu Jianfeng, Wang Yujing, Sun Hao, Wei Furu, Deng Denvy, Zhang Qi. Arxiv 2023

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Fine Tuning GPT Has Code Model Architecture Pretraining Methods Prompting Training Techniques

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.

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