LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering

• URL: http://arxiv.org/abs/2505.05423v3
• Date: Thu, 22 May 2025 10:01:15 GMT
• Title: LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering
• Authors: Ran Zhang, Wei Zhao, Lieve Macken, Steffen Eger,
• Abstract summary: LiTransProQA is a novel, reference-free, LLM-based question-answering framework designed for literary translation evaluation.<n>It integrates insights from professional literary translators and researchers, focusing on literary devices, cultural understanding, and authorial voice.<n>LiTransProQA substantially outperforms current metrics, achieving up to 0.07 gain in correlation and surpassing the best state-of-the-art metrics by over 15 points in adequacy assessments.
• Score: 21.28047224832753
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The impact of Large Language Models (LLMs) has extended into literary domains. However, existing evaluation metrics prioritize mechanical accuracy over artistic expression and tend to overrate machine translation as being superior to human translation from experienced professionals. In the long run, this bias could result in an irreversible decline in translation quality and cultural authenticity. In response to the urgent need for a specialized literary evaluation metric, we introduce LiTransProQA, a novel, reference-free, LLM-based question-answering framework designed for literary translation evaluation. LiTransProQA uniquely integrates insights from professional literary translators and researchers, focusing on critical elements in literary quality assessment such as literary devices, cultural understanding, and authorial voice. Our extensive evaluation shows that while literary-finetuned XCOMET-XL yields marginal gains, LiTransProQA substantially outperforms current metrics, achieving up to 0.07 gain in correlation and surpassing the best state-of-the-art metrics by over 15 points in adequacy assessments. Incorporating professional translator insights as weights further improves performance, highlighting the value of translator inputs. Notably, LiTransProQA reaches human-level evaluation performance comparable to trained student evaluators. It shows broad applicability to open-source models like LLaMa3.3-70b and Qwen2.5-32b, indicating its potential as an accessible and training-free tool for evaluating literary translations that require local processing due to copyright or ethical considerations. The code and datasets are available under: https://github.com/zhangr2021/TransProQA.

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