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        <title>RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval</title>
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        <namePart type="given">Radu</namePart>
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    <abstract>The paper details our approach to SemEval 2025 Shared Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval.We investigate how large language models (LLMs) designed for general-purpose retrieval via text-embeddings can be adapted for fact-checked claim retrieval across multiple languages, including scenarios where the query and fact-check are in different languages. The experiments involve fine-tuning with a contrastive objective, resulting in notable gains in both accuracy and efficiency over the baseline retrieval model. We evaluate cost-effective techniques such as LoRA and QLoRA and Prompt Tuning.Additionally, we demonstrate the benefits of Matryoshka embeddings in minimizing the memory footprint of stored embeddings, reducing the system requirements for a fact-checking system.</abstract>
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%0 Conference Proceedings
%T RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval
%A Chivereanu, Radu -. Gabriel
%A Tufis, Dan
%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 chivereanu-tufis-2025-racai
%X The paper details our approach to SemEval 2025 Shared Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval.We investigate how large language models (LLMs) designed for general-purpose retrieval via text-embeddings can be adapted for fact-checked claim retrieval across multiple languages, including scenarios where the query and fact-check are in different languages. The experiments involve fine-tuning with a contrastive objective, resulting in notable gains in both accuracy and efficiency over the baseline retrieval model. We evaluate cost-effective techniques such as LoRA and QLoRA and Prompt Tuning.Additionally, we demonstrate the benefits of Matryoshka embeddings in minimizing the memory footprint of stored embeddings, reducing the system requirements for a fact-checking system.
%U https://aclanthology.org/2025.semeval-1.77/
%P 551-557

Markdown (Informal)

[RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://aclanthology.org/2025.semeval-1.77/) (Chivereanu & Tufis, SemEval 2025)

• RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval (Chivereanu & Tufis, SemEval 2025)

ACL

• Radu - Gabriel Chivereanu and Dan Tufis. 2025. RACAI at SemEval-2025 Task 7: Efficient adaptation of Large Language Models for Multilingual and Crosslingual Fact-Checked Claim Retrieval. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 551–557, Vienna, Austria. Association for Computational Linguistics.