@inproceedings{baez-saggion-2023-lsllama,
title = "{LSL}lama: Fine-Tuned {LL}a{MA} for Lexical Simplification",
author = "Baez, Anthony and
Saggion, Horacio",
editor = "{\v{S}}tajner, Sanja and
Saggio, Horacio and
Shardlow, Matthew and
Alva-Manchego, Fernando",
booktitle = "Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.tsar-1.10",
pages = "102--108",
abstract = "Generative Large Language Models (LLMs), such as GPT-3, have become increasingly effective and versatile in natural language processing (NLP) tasks. One such task is Lexical Simplification, where state-of-the-art methods involve complex, multi-step processes which can use both deep learning and non-deep learning processes. LLaMA, an LLM with full research access, holds unique potential for the adaption of the entire LS pipeline. This paper details the process of fine-tuning LLaMA to create LSLlama, which performs comparably to previous LS baseline models LSBert and UniHD.",
}
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%0 Conference Proceedings
%T LSLlama: Fine-Tuned LLaMA for Lexical Simplification
%A Baez, Anthony
%A Saggion, Horacio
%Y Štajner, Sanja
%Y Saggio, Horacio
%Y Shardlow, Matthew
%Y Alva-Manchego, Fernando
%S Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F baez-saggion-2023-lsllama
%X Generative Large Language Models (LLMs), such as GPT-3, have become increasingly effective and versatile in natural language processing (NLP) tasks. One such task is Lexical Simplification, where state-of-the-art methods involve complex, multi-step processes which can use both deep learning and non-deep learning processes. LLaMA, an LLM with full research access, holds unique potential for the adaption of the entire LS pipeline. This paper details the process of fine-tuning LLaMA to create LSLlama, which performs comparably to previous LS baseline models LSBert and UniHD.
%U https://aclanthology.org/2023.tsar-1.10
%P 102-108
Markdown (Informal)
[LSLlama: Fine-Tuned LLaMA for Lexical Simplification](https://aclanthology.org/2023.tsar-1.10) (Baez & Saggion, TSAR-WS 2023)
ACL