@inproceedings{skottis-etal-2025-ragthoven,
    title = "{RAG}thoven at {S}em{E}val 2025 - Task 2: Enhancing Entity-Aware Machine Translation with Large Language Models, Retrieval Augmented Generation and Function Calling",
    author = "Skottis, Demetris  and
      Karetka, Gregor  and
      Suppa, Marek",
    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://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.305/",
    pages = "2342--2351",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a system for SemEval 2025 Task 2 on entity-aware machine translation, integrating GPT-4o with Wikidata-based translations, retrieval augmented generation (RAG), and function calling. Implemented in RAGthoven, a lightweight yet powerful toolkit, our approach enriches source sentences with real-time external knowledge to address challenging or culturally specific named entities. Experiments on English-to-ten target languages show notable gains in translation quality, illustrating how LLM-based translation pipelines can leverage knowledge sources with minimal overhead. Its simplicity makes it a strong baseline for future research in entity-focused machine translation."
}