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 this https URL.

UPRISE is a model that improves the generalization of large language models through a lightweight and versatile retriever that automatically retrieves prompts for zero-shot tasks, without the need for model-specific fine-tuning or task-specific prompt engineering, and mitigates the hallucination problem in chatbots.