@inproceedings{ettaleb-etal-2025-contribution,
title = "The contribution of {LLM}s to relation extraction in the economic field",
author = "Ettaleb, Mohamed and
Kamel, Mouna and
Aussenac-Gilles, Nathalie and
Moriceau, V{\'e}ronique",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.17/",
pages = "175--183",
abstract = "Relation Extraction (RE) is a fundamental task in natural language processing, aimed at deducing semantic relationships between entities in a text. Traditional supervised extraction methods relation extraction methods involve training models to annotate tokens representing entity mentions, followed by predicting the relationship between these entities. However, recent advancements have transformed this task into a sequence-to-sequence problem. This involves converting relationships between entities into target string, which are then generated from the input text. Thus, language models now appear as a solution to this task and have already been used in numerous studies, with various levels of refinement, across different domains. The objective of the present study is to evaluate the contribution of large language models (LLM) to the task of relation extraction in a specific domain (in this case, the economic domain), compared to smaller language models. To do this, we considered as a baseline a model based on the BERT architecture, trained in this domain, and four LLM, namely FinGPT specific to the financial domain, XLNet, ChatGLM, and Llama3, which are generalists. All these models were evaluated on the same extraction task, with zero-shot for the general-purpose LLM, as well as refinements through few-shot learning and fine-tuning. The experiments showedthat the best performance in terms of F-score was achieved with fine-tuned LLM, with Llama3 achieving the highest performance."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ettaleb-etal-2025-contribution">
<titleInfo>
<title>The contribution of LLMs to relation extraction in the economic field</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mohamed</namePart>
<namePart type="family">Ettaleb</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mouna</namePart>
<namePart type="family">Kamel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathalie</namePart>
<namePart type="family">Aussenac-Gilles</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Véronique</namePart>
<namePart type="family">Moriceau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chung-Chi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Moreno-Sandoval</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jimin</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qianqian</namePart>
<namePart type="family">Xie</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sophia</namePart>
<namePart type="family">Ananiadou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsin-Hsi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Relation Extraction (RE) is a fundamental task in natural language processing, aimed at deducing semantic relationships between entities in a text. Traditional supervised extraction methods relation extraction methods involve training models to annotate tokens representing entity mentions, followed by predicting the relationship between these entities. However, recent advancements have transformed this task into a sequence-to-sequence problem. This involves converting relationships between entities into target string, which are then generated from the input text. Thus, language models now appear as a solution to this task and have already been used in numerous studies, with various levels of refinement, across different domains. The objective of the present study is to evaluate the contribution of large language models (LLM) to the task of relation extraction in a specific domain (in this case, the economic domain), compared to smaller language models. To do this, we considered as a baseline a model based on the BERT architecture, trained in this domain, and four LLM, namely FinGPT specific to the financial domain, XLNet, ChatGLM, and Llama3, which are generalists. All these models were evaluated on the same extraction task, with zero-shot for the general-purpose LLM, as well as refinements through few-shot learning and fine-tuning. The experiments showedthat the best performance in terms of F-score was achieved with fine-tuned LLM, with Llama3 achieving the highest performance.</abstract>
<identifier type="citekey">ettaleb-etal-2025-contribution</identifier>
<location>
<url>https://aclanthology.org/2025.finnlp-1.17/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>175</start>
<end>183</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The contribution of LLMs to relation extraction in the economic field
%A Ettaleb, Mohamed
%A Kamel, Mouna
%A Aussenac-Gilles, Nathalie
%A Moriceau, Véronique
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ettaleb-etal-2025-contribution
%X Relation Extraction (RE) is a fundamental task in natural language processing, aimed at deducing semantic relationships between entities in a text. Traditional supervised extraction methods relation extraction methods involve training models to annotate tokens representing entity mentions, followed by predicting the relationship between these entities. However, recent advancements have transformed this task into a sequence-to-sequence problem. This involves converting relationships between entities into target string, which are then generated from the input text. Thus, language models now appear as a solution to this task and have already been used in numerous studies, with various levels of refinement, across different domains. The objective of the present study is to evaluate the contribution of large language models (LLM) to the task of relation extraction in a specific domain (in this case, the economic domain), compared to smaller language models. To do this, we considered as a baseline a model based on the BERT architecture, trained in this domain, and four LLM, namely FinGPT specific to the financial domain, XLNet, ChatGLM, and Llama3, which are generalists. All these models were evaluated on the same extraction task, with zero-shot for the general-purpose LLM, as well as refinements through few-shot learning and fine-tuning. The experiments showedthat the best performance in terms of F-score was achieved with fine-tuned LLM, with Llama3 achieving the highest performance.
%U https://aclanthology.org/2025.finnlp-1.17/
%P 175-183
Markdown (Informal)
[The contribution of LLMs to relation extraction in the economic field](https://aclanthology.org/2025.finnlp-1.17/) (Ettaleb et al., FinNLP 2025)
ACL
- Mohamed Ettaleb, Mouna Kamel, Nathalie Aussenac-Gilles, and Véronique Moriceau. 2025. The contribution of LLMs to relation extraction in the economic field. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 175–183, Abu Dhabi, UAE. Association for Computational Linguistics.