CHILL at SemEval-2025 Task 2: You Can't Just Throw Entities and Hope -- Make Your LLM to Get Them Right

Jaebok Lee; Yonghyun Ryu; Seongmin Park; Yoonjung Choi

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arXiv · 2506.13070

CHILL at SemEval-2025 Task 2: You Can't Just Throw Entities and Hope -- Make Your LLM to Get Them Right

Jaebok Lee·Yonghyun Ryu·Seongmin Park·Yoonjung Choi

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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.

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Jaebok Lee, Yonghyun Ryu, Seongmin Park, Yoonjung Choi. 2025-06-16. CHILL at SemEval-2025 Task 2: You Can't Just Throw Entities and Hope -- Make Your LLM to Get Them Right. https://arxiv.org/abs/2506.13070

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