AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

arXiv:2303.16854v2 Announce Type: replace
Abstract: Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance. However, data annotation is time-consuming and expensive, especially when the task involves a large amount of data or requires specialized domains. Recently, GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. In this paper, we first claim that large language models (LLMs), such as GPT-3.5, can serve as an excellent crowdsourced annotator …

abstract annotation arxiv cs.cl data data annotation domains gpt gpt-3 gpt-3.5 however language language models language processing large language large language models machine machine learning machine learning models making natural natural language natural language processing nlp performance processing series tasks train type