@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Lexical Complexity Prediction through Few-shot Learning with Gpt-3
%A Ortiz-Zambrano, Jenny Alexandra
%A Espín-Riofrío, César Humberto
%A Montejo-Ráez, Arturo
%Y Nunzio, Giorgio Maria Di
%Y Vezzani, Federica
%Y Ermakova, Liana
%Y Azarbonyad, Hosein
%Y Kamps, Jaap
%S Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F ortiz-zambrano-etal-2024-enhancing
%X 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.
%U https://aclanthology.org/2024.determit-1.7
%P 68-76
Markdown (Informal)
[Enhancing Lexical Complexity Prediction through Few-shot Learning with Gpt-3](https://aclanthology.org/2024.determit-1.7) (Ortiz-Zambrano et al., DeTermIt-WS 2024)
ACL