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Annotated data plays a critical role in Natural Language Processing (NLP) intraining models and evaluating their performance. Given recent developments inLarge Language Models (LLMs), models such as ChatGPT demonstrate zero-shotcapability on many text-annotation tasks, comparable with or even exceedinghuman annotators. Such LLMs can serve as alternatives for manual annotation,due to lower costs and higher scalability. However, limited work has leveragedLLMs as complementary annotators, nor explored how annotation work is bestallocated among humans and LLMs to achieve both quality and cost objectives. Wepropose CoAnnotating, a novel paradigm for Human-LLM co-annotation ofunstructured texts at scale. Under this framework, we utilize uncertainty toestimate LLMs' annotation capability. Our empirical study shows CoAnnotating tobe an effective means to allocate work from results on different datasets, withup to 21% performance improvement over random baseline. For codeimplementation, see https://github.com/SALT-NLP/CoAnnotating.

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation