Zero-shot prompting is a technique in which an AI model is given a task or question without any prior examples or specific training on that task, relying solely on its pre-existing knowledge to generate a response.
As a result, AI experts use techniques like zero-shot and few-shot prompting to improve the effectiveness of transformer-based neural networks. Prompting is the process of asking the right questions to LLMs for ensuring better personalization of responses. It helps in creating precise cues and in...
本文提出的instruction tuning,是对之前流行的两种范式“pretrain–finetune”和“prompting”的综合。第一种范式,是特定于任务的,采用什么样的标注数据,便能提升什么样的任务效果,第二种范式,是通用的,无需微调,借助in-context learning,推理时在各种任务上皆可展现zero-shot能力。instruction tuning,则是通过对一部分...
LLM with task-specific few-shot or zero-shot prompting对于那些需要多步推理的任务是有困难的\righta...
Zero-Shot Prompting In natural language processing models, zero-shot prompting means providing a prompt that is not part of the training data to the model, but the model can generate a result that you desire. This promising technique makes large language models useful for many tasks. ...
Our findings suggest that monolingual transformer-based models consistently outperform other models, even in zero and few-shot scenarios. To foster continued exploration, we intend to make this dataset and our research tools publicly available to the broader research community. 展开 ...
Zero-shot learningin NLP allows a pre-trained LLM to generate responses to tasks that it hasn’t been specifically trained for. In this technique, the model is provided with an input text and a prompt that describes the expected output from the model in natural language. Th...
In this paper, we investigate this question in the context of zero-shot prompting and few-shot model fine-tuning, with the aim of reducing the needfor human-annotated training samples as much as possible.Scius-Bertrand, AnnaUniversity of FribourgJungo, Michael...
Large language models (LLMs) have proven effective in various NLP tasks. Fine-tuning LLMs for downstream tasks is challenging due to limited access to model parameters. Zero-shot-CoT prompting has been successful in solving multi-step reasoning tasks but suffers from calculation errors, missing-st...
In the new paper UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation, a Microsoft research team introduces a novel approach that tunes a lightweight and versatile retriever to retrieve prompts for any given task input to improve the zero-shot performance of LLMs....