@inproceedings{ortiz-zambrano-etal-2024-enhancing,
    title = "Enhancing Lexical Complexity Prediction through Few-shot Learning with Gpt-3",
    author = "Ortiz-Zambrano, Jenny Alexandra  and
      Esp{\'\i}n-Riofr{\'\i}o, C{\'e}sar Humberto  and
      Montejo-R{\'a}ez, Arturo",
    editor = "Nunzio, Giorgio Maria Di  and
      Vezzani, Federica  and
      Ermakova, Liana  and
      Azarbonyad, Hosein  and
      Kamps, Jaap",
    booktitle = "Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.determit-1.7",
    pages = "68--76",
    abstract = "This paper describes an experiment to evaluate the ability of the GPT-3 language model to classify terms regarding their lexical complexity. This was achieved through the creation and evaluation of different versions of the model: text-Davinci-002 y text-Davinci-003 and prompts for few-shot learning to determine the complexity of the words. The results obtained on the CompLex dataset achieve a minimum average error of 0.0856. Although this is not better than the state of the art (which is 0.0609), it is a performing and promising approach to lexical complexity prediction without the need for model fine-tuning.",
}

This paper describes an experiment to evaluate the ability of the GPT-3 language model to classify terms regarding their lexical complexity. This was achieved through the creation and evaluation of different versions of the model: text-Davinci-002 y text-Davinci-003 and prompts for few-shot learning to determine the complexity of the words. The results obtained on the CompLex dataset achieve a minimum average error of 0.0856. Although this is not better than the state of the art (which is 0.0609), it is a performing and promising approach to lexical complexity prediction without the need for model fine-tuning.
Ortiz-Zambrano, Jenny Alex ra Esp{\'\i}n-Riofr{\'\i}o, C{\'e}sar Humberto Montejo-R{\'a}ez, Arturo
Enhancing Lexical Complexity Prediction through Few-shot Learning with Gpt-3