mlsanityData to 7 Oct 2026

Gender-specific Machine Translation with Large Language Models

arXiv 2309.03175 ↗First posted 6 Sep 2023cs.CLcited by 3

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Abstract (arXiv)

While machine translation (MT) systems have seen significant improvements, it is still common for translations to reflect societal biases, such as gender bias. Decoder-only Large Language Models (LLMs) have demonstrated potential in MT, albeit with performance slightly lagging behind traditional encoder-decoder Neural Machine Translation (NMT) systems. However, LLMs offer a unique advantage: the ability to control the properties of the output through prompts. In this study, we leverage this flexibility to explore LLaMa's capability to produce gender-specific translations. Our results indicate that LLaMa can generate gender-specific translations with translation accuracy and gender bias comparable to NLLB, a state-of-the-art multilingual NMT system. Furthermore, our experiments reveal that LLaMa's gender-specific translations rely on coreference resolution to determine gender, showing higher gender variance in gender-ambiguous datasets but maintaining consistency in less ambiguous contexts. This research investigates the potential and challenges of using LLMs for gender-specific translations as an instance of the controllability of outputs offered by LLMs.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 57 references that are in the map. First posted on arXiv 6 Sep 2023.

Builds on

40 of 57 references.

References · 40

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28 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translationcited by 8
  2. 2025When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Contentcited by 0
  3. 2026Mitigating Gender Bias in English to Romanian Machine Translationcited by 0