Aligning Translation-Specific Understanding to General Understanding in Large Language Models

ACL ARR 2024 June Submission5269 Authors

16 Jun 2024 (modified: 07 Aug 2024)ACL ARR 2024 June SubmissionEveryoneRevisionsBibTeXCC BY 4.0
Abstract: Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this study reveals the misalignment between the translation-specific understanding and the general understanding inside LLMs. This understanding misalignment leads to LLMs mistakenly or literally translating some complicated concepts that they accurately comprehend in the general scenarios (e.g., QA). To align the translation-specific understanding to the general one, we propose a novel translation process, DUAT (Difficult words Understanding Aligned Translation), explicitly incorporating the general understanding on the complicated content incurring inconsistent understandings to guide the translation. Specifically, DUAT performs cross-lingual interpretation for the difficult-to-translate words and enhances the translation with the generated interpretations. Furthermore, we reframe the external tools to improve DUAT in detecting difficult words and generating helpful interpretations. We conduct experiments on the self-constructed benchmark Challenge-WMT, consisting of samples that are prone to mistranslation. Human evaluation results on high-resource and low-resource language pairs indicate that DUAT significantly facilitates the understanding alignment, which improves the translation quality (up to +3.85 COMET) and reduces translation literalness by -25% ∼ -51%.
Paper Type: Long
Research Area: Machine Translation
Research Area Keywords: large language models
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources
Languages Studied: English,Chinese,Estonian,Islandic
Submission Number: 5269
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