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Many natural language processing (NLP) tasks rely on labeled data to trainmachine learning models to achieve high performance. However, data annotationcan be a time-consuming and expensive process, especially when the taskinvolves a large amount of data or requires specialized domains. Recently,GPT-3.5 series models have demonstrated remarkable few-shot and zero-shotability across various NLP tasks. In this paper, we first claim that largelanguage models (LLMs), such as GPT-3.5, can serve as an excellent crowdsourcedannotator by providing them with sufficient guidance and demonstrated examples.To make LLMs to be better annotators, we propose a two-step approach,'explain-then-annotate'. To be more precise, we begin by creating prompts forevery demonstrated example, which we subsequently utilize to prompt a LLM toprovide an explanation for why the specific ground truth answer/label waschosen for that particular example. Following this, we construct the few-shotchain-of-thought prompt with the self-generated explanation and employ it toannotate the unlabeled data. We conduct experiments on three tasks, includinguser input and keyword relevance assessment, BoolQ and WiC. The annotationresults from GPT-3.5 surpasses those from crowdsourced annotation for userinput and keyword relevance assessment. Additionally, for the other two tasks,GPT-3.5 achieves results that are comparable to those obtained throughcrowdsourced annotation.

AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators