# A Prompt Response to the Demand for Automatic Gender-Neutral Translation

arXiv: https://arxiv.org/abs/2402.06041 · HTML: https://mlsanity.com/paper/2402.06041/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 60 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Prompting PaLM for Translation: Assessing Strategies and Performance](https://mlsanity.com/paper/2211.09102/) (2022)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Toward Gender-Inclusive Coreference Resolution](https://mlsanity.com/paper/1910.13913/) (2019)
- [On Measuring Gender Bias in Translation of Gender-neutral Pronouns](https://mlsanity.com/paper/1905.11684/) (2019)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- [NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives](https://mlsanity.com/paper/2109.06105/) (2021)
- [They, Them, Theirs: Rewriting with Gender-Neutral English](https://mlsanity.com/paper/2102.06788/) (2021)
- [Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It](https://mlsanity.com/paper/2010.05332/) (2020)
- [Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challenges](https://mlsanity.com/paper/2301.10075/) (2023)
- [Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus](https://mlsanity.com/paper/2310.05294/) (2023)
- [What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns](https://mlsanity.com/paper/2305.16051/) (2023)
- [How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns](https://mlsanity.com/paper/2204.10281/) (2022)
- [Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model](https://mlsanity.com/paper/2305.11140/) (2023)
- [Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability](https://mlsanity.com/paper/2308.01391/) (2023)
- [RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation](https://mlsanity.com/paper/2305.17131/) (2023)
- [Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES](https://mlsanity.com/paper/2310.19345/) (2023)
- [Improving Gender Translation Accuracy with Filtered Self-Training](https://mlsanity.com/paper/2104.07695/) (2021)

## Built on by (most cited first, 12 of 12)

- [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024)
- [The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs](https://mlsanity.com/paper/2407.18786/) (2024)
- [Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models](https://mlsanity.com/paper/2405.08477/) (2024)
- [The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification](https://mlsanity.com/paper/2409.17929/) (2024)
- [Generating Gender Alternatives in Machine Translation](https://mlsanity.com/paper/2407.20438/) (2024)
- [Gender Bias in English-to-Greek Machine Translation](https://mlsanity.com/paper/2506.09558/) (2025)
- [Gender-Neutral Machine Translation Strategies in Practice](https://mlsanity.com/paper/2506.15676/) (2025)
- [An LLM-as-a-judge Approach for Scalable Gender-Neutral Translation Evaluation](https://mlsanity.com/paper/2504.11934/) (2025)
- [What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Models](https://mlsanity.com/paper/2512.08440/) (2025)
- [WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles](https://mlsanity.com/paper/2411.06194/) (2024)
- [MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation](https://mlsanity.com/paper/2412.11615/) (2024)
- [Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offs](https://mlsanity.com/paper/2509.13480/) (2025)

## Abstract (arXiv)

Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.