@inproceedings{haroon-etal-2025-caisa,
title = "{CAISA} at {S}em{E}val-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval",
author = "Haroon, Muqaddas and
Ashraf, Shaina and
Baris, Ipek and
Flek, Lucie",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.183/",
pages = "1377--1382",
ISBN = "979-8-89176-273-2",
abstract = "We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="haroon-etal-2025-caisa">
<titleInfo>
<title>CAISA at SemEval-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval</title>
</titleInfo>
<name type="personal">
<namePart type="given">Muqaddas</namePart>
<namePart type="family">Haroon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shaina</namePart>
<namePart type="family">Ashraf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ipek</namePart>
<namePart type="family">Baris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lucie</namePart>
<namePart type="family">Flek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Rosenthal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aiala</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Zampieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-273-2</identifier>
</relatedItem>
<abstract>We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed.</abstract>
<identifier type="citekey">haroon-etal-2025-caisa</identifier>
<location>
<url>https://aclanthology.org/2025.semeval-1.183/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>1377</start>
<end>1382</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CAISA at SemEval-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval
%A Haroon, Muqaddas
%A Ashraf, Shaina
%A Baris, Ipek
%A Flek, Lucie
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F haroon-etal-2025-caisa
%X We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed.
%U https://aclanthology.org/2025.semeval-1.183/
%P 1377-1382
Markdown (Informal)
[CAISA at SemEval-2025 Task 7: Multilingual and Cross-lingual Fact-Checked Claim Retrieval](https://aclanthology.org/2025.semeval-1.183/) (Haroon et al., SemEval 2025)
ACL