A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications

• URL: http://arxiv.org/abs/2503.20302v1
• Date: Wed, 26 Mar 2025 08:01:35 GMT
• Title: A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
• Authors: Sunayana Sitaram, Adrian de Wynter, Isobel McCrum, Qilong Gu, Si-Qing Chen,
• Abstract summary: Misgendering is the act of referring to someone by a gender that does not match their chosen identity.<n>English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun they''
• Score: 12.5856659067182
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person's sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun ``they''. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard large language model-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures.

Related papers

• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies [75.85462924188076]
  Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM) We find that misgendering is significantly influenced by Byte-Pair (BPE) tokenization. We propose two techniques: (1) pronoun tokenization parity, a method to enforce consistent tokenization across gendered pronouns, and (2) utilizing pre-existing LLM pronoun knowledge to improve neopronoun proficiency.
  arXiv  Detail & Related papers  (2023-12-19T01:28:46Z)
• On Evaluating and Mitigating Gender Biases in Multilingual Settings [5.248564173595024]
  We investigate some of the challenges with evaluating and mitigating biases in multilingual settings. We first create a benchmark for evaluating gender biases in pre-trained masked language models. We extend various debiasing methods to work beyond English and evaluate their effectiveness for SOTA massively multilingual models.
  arXiv  Detail & Related papers  (2023-07-04T06:23:04Z)
• MISGENDERED: Limits of Large Language Models in Understanding Pronouns [46.276320374441056]
  We evaluate popular language models for their ability to correctly use English gender-neutral pronouns. We introduce MISGENDERED, a framework for evaluating large language models' ability to correctly use preferred pronouns.
  arXiv  Detail & Related papers  (2023-06-06T18:27:52Z)
• Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation [12.376309678270275]
  bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.
  arXiv  Detail & Related papers  (2023-05-26T13:51:50Z)
• Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation [28.471506840241602]
  Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. We propose a bias mitigation method based on a novel approach. Gender-Aware Contrastive Learning, GACL, encodes contextual gender information into the representations of non-explicit gender words.
  arXiv  Detail & Related papers  (2023-05-23T12:53:39Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)
• INCLUSIFY: A benchmark and a model for gender-inclusive German [0.0]
  Gender-inclusive language is important for achieving gender equality in languages with gender inflections. A handful of tools have been developed to help people use gender-inclusive language. We present a dataset and measures for benchmarking them, and present a model that implements these tasks.
  arXiv  Detail & Related papers  (2022-12-05T19:37:48Z)
• They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
  We perform a case study on the singular they, a common way to promote gender inclusion in English. We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
  arXiv  Detail & Related papers  (2021-02-12T21:47:48Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.