<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="baez-saggion-2023-lsllama">
    <titleInfo>
        <title>LSLlama: Fine-Tuned LLaMA for Lexical Simplification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Anthony</namePart>
        <namePart type="family">Baez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Horacio</namePart>
        <namePart type="family">Saggion</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sanja</namePart>
            <namePart type="family">Štajner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Horacio</namePart>
            <namePart type="family">Saggio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Matthew</namePart>
            <namePart type="family">Shardlow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">baez-saggion-2023-lsllama</identifier>
    <location>
        <url>https://aclanthology.org/2023.tsar-1.10/</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>102</start>
            <end>108</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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 2023)

• LSLlama: Fine-Tuned LLaMA for Lexical Simplification (Baez & Saggion, TSAR 2023)

ACL

• Anthony Baez and Horacio Saggion. 2023. LSLlama: Fine-Tuned LLaMA for Lexical Simplification. In Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability, pages 102–108, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.