mlsanityData to 7 Oct 2026

Gender Bias in Machine Translation and The Era of Large Language Models

arXiv 2401.10016 ↗First posted 18 Jan 2024cs.CL cs.AI cs.CYcited by 4

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Abstract (arXiv)

This chapter examines the role of Machine Translation in perpetuating gender bias, highlighting the challenges posed by cross-linguistic settings and statistical dependencies. A comprehensive overview of relevant existing work related to gender bias in both conventional Neural Machine Translation approaches and Generative Pretrained Transformer models employed as Machine Translation systems is provided. Through an experiment using ChatGPT (based on GPT-3.5) in an English-Italian translation context, we further assess ChatGPT's current capacity to address gender bias. The findings emphasize the ongoing need for advancements in mitigating bias in Machine Translation systems and underscore the importance of fostering fairness and inclusivity in language technologies.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 18 Jan 2024.

Builds on

37 of 52 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  6. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  7. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  9. 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
  10. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  11. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  12. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
25 more
  1. 2020Intrinsic Bias Metrics Do Not Correlate with Application Biascited by 148
  2. 2023Towards Making the Most of ChatGPT for Machine Translationcited by 138
  3. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  4. 2021Gender Bias in Machine Translationcited by 116
  5. 2018Identifying and Controlling Important Neurons in Neural Machine Translationcited by 110
  6. 2019Getting Gender Right in Neural Machine Translationcited by 98
  7. 2023Document-Level Machine Translation with Large Language Modelscited by 90
  8. 2023Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Modelscited by 87
  9. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  10. 2019Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniquescited by 65
  11. 2019On Measuring Gender Bias in Translation of Gender-neutral Pronounscited by 59
  12. 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
  13. 2021NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternativescited by 46
  14. 2019Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translationcited by 45
  15. 2021They, Them, Theirs: Rewriting with Gender-Neutral Englishcited by 44
  16. 2020Mitigating Gender Bias in Machine Translation with Target Gender Annotationscited by 42
  17. 2020Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make Itcited by 40
  18. 2023ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languagescited by 33
  19. 2023How to Design Translation Prompts for ChatGPT: An Empirical Studycited by 31
  20. 2019Post-editese: an Exacerbated Translationesecited by 29
  21. 2019Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injectioncited by 27
  22. 2022Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semanticscited by 24
  23. 2022Toward Human-Like Evaluation for Natural Language Generation with Error Analysiscited by 18
  24. 2021GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomenacited by 16
  25. 2023Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clausescited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMscited by 7
  2. 2025What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Modelscited by 2
  3. 2026Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architecturescited by 1
  4. 2026Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detectioncited by 0