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May 8, 2026Automatic Documentation and Mathematical Linguistics

Assessing the Quality of Large Language Models in Machine Translation Tasks

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Authors

АМА. В. МыльниковаЛМЛ. А. Мыльников

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Overview

Comparative evaluation assesses translation quality across large language models, highlighting modern advancements.

Key Points

  • The aim is to compare the quality of translations produced by various large language models against established benchmarks.
  • Comparative evaluation of translations from six large language models: DeepSeek, Grok, Mistral, Qwen, GigaChat, and Yandex.
  • Assessment of translation quality using both quantitative metrics (BLEU, METEOR, chrF) and qualitative expert analysis.
  • Comparisons made against Google Translate and based on criteria of adequacy, equivalence, and harmony.
  • Modern large language models outperform classical machine translation, addressing traditional challenges effectively.
  • LLMs demonstrate significant capability in translating expressive linguistic elements such as phraseologisms and puns.
  • Expert evaluation highlights improved adequacy and equivalence in translations by LLMs compared to reference translations.

Cite This Study

Мыльникова et al. (2026) studied this question.

synapsesocial.com/papers/69fd7d94bfa21ec5bbf05f70https://doi.org/10.3103/s0005105526700020
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