git clone https://github.com/DAMO-NLP-SG/LLM-Data-Annotator
cd LLM-Data-Annotator
unzip Data&Prompts&Codes.zip

pip install -r requirements.txt

cd FewRel/code

cd SST2

cd ASTE

cd CrossNER

def prompt_text(prompt_content):
    result = openai.Completion.create(
              model="text-davinci-003",
              prompt=prompt_content,
              max_tokens=2000,
              temperature=1.0
            )
    result_text = result['choices'][0]['text'].strip()
    return result_text

@inproceedings{ding-etal-2023-gpt,
    title = "Is {GPT}-3 a Good Data Annotator?",
    author = "Ding, Bosheng  and
      Qin, Chengwei  and
      Liu, Linlin  and
      Chia, Yew Ken  and
      Li, Boyang  and
      Joty, Shafiq  and
      Bing, Lidong",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.626",
    doi = "10.18653/v1/2023.acl-long.626",
    pages = "11173--11195",
    abstract = "Data annotation is the process of labeling data that could be used to train machine learning models. Having high quality annotation is crucial, as it allows the model to learn the relationship between the input data and the desired output. GPT-3, a large-scale language model developed by OpenAI, has demonstrated im- impressive zero- and few-shot performance on a wide range of NLP tasks. It is therefore natural to wonder whether it can be used to effectively annotate data for NLP tasks. In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks. Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP.",
}