@inproceedings{chatzikyriakidis-natsina-2025-poetry,
title = "Poetry in {RAG}s: {M}odern {G}reek interwar poetry generation using {RAG} and contrastive training",
author = "Chatzikyriakidis, Stergios and
Natsina, Anastasia",
editor = {H{\"a}m{\"a}l{\"a}inen, Mika and
{\"O}hman, Emily and
Bizzoni, Yuri and
Miyagawa, So and
Alnajjar, Khalid},
booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
month = may,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlp4dh-1.22/",
doi = "10.18653/v1/2025.nlp4dh-1.22",
pages = "257--264",
ISBN = "979-8-89176-234-3",
abstract = "In this paper, we discuss Modern Greek poetry generation in the style of lesser known Greek poets of the interwar period. The paper proposes the use of Retrieval-Augmented Generation (RAG) to automatically generate poetry using Large Language Models (LLMs). A corpus of Greek interwar poetry is used and prompts exemplifying the poet{'}s style with respect to a theme are created. These are then fed to an LLM. The results are compared to pure LLM generation and expert evaluators score poems across a number of parameters. Objective metrics such as Vocabulary Density, Average words per Sentence and Readability Index are also used to assess the performance of the models. RAG-assisted models show potential in enhancing poetry generation across a number of parameters. Base LLM models appear quite consistent across a number of categories, while the RAG model that is furthermore contrastive shows the worst performance of the three."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chatzikyriakidis-natsina-2025-poetry">
<titleInfo>
<title>Poetry in RAGs: Modern Greek interwar poetry generation using RAG and contrastive training</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stergios</namePart>
<namePart type="family">Chatzikyriakidis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anastasia</namePart>
<namePart type="family">Natsina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mika</namePart>
<namePart type="family">Hämäläinen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="family">Öhman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuri</namePart>
<namePart type="family">Bizzoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">So</namePart>
<namePart type="family">Miyagawa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalid</namePart>
<namePart type="family">Alnajjar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-234-3</identifier>
</relatedItem>
<abstract>In this paper, we discuss Modern Greek poetry generation in the style of lesser known Greek poets of the interwar period. The paper proposes the use of Retrieval-Augmented Generation (RAG) to automatically generate poetry using Large Language Models (LLMs). A corpus of Greek interwar poetry is used and prompts exemplifying the poet’s style with respect to a theme are created. These are then fed to an LLM. The results are compared to pure LLM generation and expert evaluators score poems across a number of parameters. Objective metrics such as Vocabulary Density, Average words per Sentence and Readability Index are also used to assess the performance of the models. RAG-assisted models show potential in enhancing poetry generation across a number of parameters. Base LLM models appear quite consistent across a number of categories, while the RAG model that is furthermore contrastive shows the worst performance of the three.</abstract>
<identifier type="citekey">chatzikyriakidis-natsina-2025-poetry</identifier>
<identifier type="doi">10.18653/v1/2025.nlp4dh-1.22</identifier>
<location>
<url>https://aclanthology.org/2025.nlp4dh-1.22/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>257</start>
<end>264</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Poetry in RAGs: Modern Greek interwar poetry generation using RAG and contrastive training
%A Chatzikyriakidis, Stergios
%A Natsina, Anastasia
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Bizzoni, Yuri
%Y Miyagawa, So
%Y Alnajjar, Khalid
%S Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-234-3
%F chatzikyriakidis-natsina-2025-poetry
%X In this paper, we discuss Modern Greek poetry generation in the style of lesser known Greek poets of the interwar period. The paper proposes the use of Retrieval-Augmented Generation (RAG) to automatically generate poetry using Large Language Models (LLMs). A corpus of Greek interwar poetry is used and prompts exemplifying the poet’s style with respect to a theme are created. These are then fed to an LLM. The results are compared to pure LLM generation and expert evaluators score poems across a number of parameters. Objective metrics such as Vocabulary Density, Average words per Sentence and Readability Index are also used to assess the performance of the models. RAG-assisted models show potential in enhancing poetry generation across a number of parameters. Base LLM models appear quite consistent across a number of categories, while the RAG model that is furthermore contrastive shows the worst performance of the three.
%R 10.18653/v1/2025.nlp4dh-1.22
%U https://aclanthology.org/2025.nlp4dh-1.22/
%U https://doi.org/10.18653/v1/2025.nlp4dh-1.22
%P 257-264
Markdown (Informal)
[Poetry in RAGs: Modern Greek interwar poetry generation using RAG and contrastive training](https://aclanthology.org/2025.nlp4dh-1.22/) (Chatzikyriakidis & Natsina, NLP4DH 2025)
ACL