@inproceedings{lee-etal-2025-chill,
title = "{CHILL} at {S}em{E}val-2025 Task 2: You Can{'}t Just Throw Entities and {H}ope{---}{M}ake Your {LLM} to Get Them Right",
author = "Lee, Jaebok and
Ryu, Yonghyun and
Park, Seongmin and
Choi, Yoonjung",
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-1.75/",
pages = "539--545",
ISBN = "979-8-89176-273-2",
abstract = "In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT).Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs).A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations."
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<abstract>In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT).Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs).A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations.</abstract>
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%0 Conference Proceedings
%T CHILL at SemEval-2025 Task 2: You Can’t Just Throw Entities and Hope—Make Your LLM to Get Them Right
%A Lee, Jaebok
%A Ryu, Yonghyun
%A Park, Seongmin
%A Choi, Yoonjung
%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 lee-etal-2025-chill
%X In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT).Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs).A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations.
%U https://aclanthology.org/2025.semeval-1.75/
%P 539-545
Markdown (Informal)
[CHILL at SemEval-2025 Task 2: You Can’t Just Throw Entities and Hope—Make Your LLM to Get Them Right](https://aclanthology.org/2025.semeval-1.75/) (Lee et al., SemEval 2025)
ACL