From ‘Showgirls’ to ‘Performers’: Fine-tuning with Gender-inclusive Language for Bias Reduction in LLM s (2024)

Abstract: Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive.The focus of our work are gender-exclusive affixes in English, such as in ‘show-girl’ or ‘man-cave’, which can perpetuate gender stereotypes and binary conceptions of gender.We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the ‘Tiny Heap’. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.

## introduction large language models (llms) have become ubiquitous in natural language processing (nlp) due to their impressive capabilities in a variety of tasks. however, they also carry risks arising from social biases incorporated into models from the training data @xcite . well-documented among these are harmful gender biases such as reliance on stereotypes and erasure of non-binary gender identities @xcite . structural aspects of language itself and linguistic norms can reflect as well as shape societal concepts of gender @xcite . within the context of llms, encoded representations of gender inform language generation and classification decisions, thereby having the potential to influence societal concepts of gender @xcite . it is vital therefore, to ensure that llms are evaluated and trained to minimize gender bias and promote equitable representation of all genders. in english, linguistic structures have a long history of reinforcing traditional gender roles and the concept of male gender as the default @xcite . examples include the use of man to mean all humans, the indication of women's marital status in terms of address (miss, mrs., ms.), or the marking of deviation from gendered norms (male nurse, girl boss). sexist and gender-exclusive linguistic constructions have been discouraged in official style guides (apa, 2020) and their use has been in decline @xcite . however, the nature of language change is slow, with new and traditional variations existing simultaneously. given the scale of llm training data @xcite and the disproportionate representation of men within textual data @xcite , language models have the potential to proliferate and reinforce stereotypical and traditional views of gender. approaches to mitigating bias in llms have included fine-tuning with gender-inclusive language @xcite . data interventions with gender-inclusive text aim to reduce the use of binary gender terms in cases where gender is irrelevant (for example, a chairman and chairwoman do the same job) and thereby allow for association of a term with all genders (chairperson). however, the replacement of sexist and genderexclusive terminology often relies on limited lists of gender-neutral terms @xcite , and often focuses on professions @xcite . additionally, previous works on fine-tuning llms with gender-inclusive data have primarily carried out experiments with masked language models such as bert @xcite and its derivatives @xcite . in this research, we focused on expanding the coverage of gender-exclusive terminology and experimented with fine-tuning both causal and masked llms. we first exploited structural elements of english that relate to gender discrimination and exclusion in order to generate a larger catalogue of words that are unnecessarily gendered along with gender neutral alternatives. we extracted nouns with gender-marking prefixes and suffixes from a common training corpus, openweb-text2 @xcite , which was used to train llms like meta's llama2 @xcite and microsoft's mt-nlg @xcite . the distribution of extracted gender-marking nouns demonstrated clear androcentric tendencies within the corpus. we compiled gender-neutral variants for each term with a gender-marking affix to form a catalogue of 692 term pairs. this resource is just over three times larger than the size of previously available resources and could be used in assessments of gender skew within llm training corpora as well as in the replacement gender-exclusive terminology. we also developed a small-scale, multi-domain fine-tuning corpus, using our catalogue to replace gender-exclusive with genderneutral words. we also employed the neutral rewriter @xcite to replace gendered pronouns (he, she, himself etc.) with singular they. the resulting corpus was used to fine-tune three different (masked and causal) llms. the results of this process of fine-tuning with gender inclusive terminology demonstrated an overall tendency towards reduction in gender-stereotyping exhibited by the models as well as a reduction in the generation of harmful language in gendered contexts. ## bias statement the focus of this work is gender-inclusive language, and its counterpart, sexist language. sexist language, following @xcite definition of sexism, can be defined as language that clearly divides between two genders, in which one gender (masculine) is treated as hierarchically superior to the other (feminine). this superiority is expressed, for example, through the generic use of masculine gendered expressions (e.g. use of terms such as mankind, chairman to refer to people of any gender). our work is based on the assumption that sexist language in training data is one of the sources of gender bias in llms. specifically, we would expect models to favor masculine expressions over gender-neutral alternatives, creating a representational harm for people of non-masculine gender @xcite . sexist expressions additionally reinforce traditional gender roles (e.g. male nurse), therefore we would also expect models to favor gender-stereotypical expressions. moreover, since sexist language is based on a binary model of gender, we expect models to default to this. this can lead to misrepresentation and erasure of non-binary genders in downstream applications, creating allocational and representational harms for non-binary users of these systems @xcite . not adjusting llms to accurately represent the variety of genders that exist in society will contribute to the ongoing marginalization of people identifying as gender-queer @xcite . ## related work large language models (llms) have been shown to encode a variety of social biases contained in their training data @xcite , among them gender bias @xcite . due to the current prevalence of transfer learning in nlp, in which a pretrained model is fine-tuned with task-specific data, transfer learning has recently also been adapted by works that aimed to reduce gender bias in llms @xcite . in this approach, an llm is fine-tuned with data that has undergone interventions to increase gender fairness. this approach is supported by the finding that biases in fine-tuning data have a greater influence on downstream model behavior than biases in the pre-training data @xcite . previous interventions to fine-tuning data include counterfactual data augmentation (cda), in which masculine and feminine pronouns and gendered nouns are swapped for the respective other @xcite @xcite . another intervention replaces gendered words for gender-neutral words (fire fighter for fireman) or phrases containing both masculine and feminine genders (he and she for he; @xcite . this kind of intervention is not new: it rests upon a longstanding tradition of research and advocacy the field of feminist linguistics, which has been promoting changes in the lexicon to reduce gender stereotyping and masculine-default language since the 1970s @xcite @xcite . more recently such changes to the language, also called feminist language reform, have incorporated ways of adapting language to include non-binary and trans gender identities, such as the third person singular (neo)pronouns (they, xe, ze, etc.). the usage and possible modelling of this extended lexicon of pronouns within the context of nlp was analyzed by @xcite . @xcite also showed that training on data containing singular they can reduce gender bias in grammatical error correction. furthermore, @xcite and @xcite developed rule-based and neutral machine translation-based models to modify english text to render it genderneutral. @xcite neutral rewriter replaces gendered pronouns with singular they and a list of gendered nouns with neutral variants. however, while the amount of nlp research incorporating and exploring strategies of feminist language reform has grown, the queer-feminist linguistic research it is based on is, with some exceptions @xcite @xcite , rarely acknowledged and even less often informs the research itself. ## method gender bias in the english language is reflected in features such as masculine generics and is captured in datasets through, for example, skewed distributions of pronouns and profession words in the same context. however, it is also contained in structural elements of the language itself, such as gendermarking affixes. the most frequent are suffixes such as -man in spokesman, but gender can also be marked with a prefix, such as in man-power or girlboss. words marked with masculine suffixes in english, the most common replacement strategy for gendered generics is neutralisation (chairperson), because all gender identities, not just male and female, can be referred to by gender-neutral nouns. in nlp, research using gender-neutral language in the context of english llms has mainly relied on lists of common gender-neutral replacements @xcite , without taking structural processes such as affixation into account in order to broaden the coverage of these lists. in this section we first outline the process of extracting unnecessarily gendered words based on gender-marking affixes ( §4.1). we then describe the gender-neutralizing interventions to our finetuning data ( §4.2) as well as the models ( §4.3) and bias measurements used ( §4.4). ## conclusion gender-inclusive language has a long history of development and advocacy within the field of feminist linguistics, but it has only recently entered gender bias research in nlp. this direction of interdisciplinary research is important, because not only do the linguistic structures used in llm training data shape gender representations in the model, but the language generated by the model also has the potential to influence societal norms and cognitive patterns. in this paper, we presented a method of semi-automatically extracting gender-exclusive nouns based on the presence of gender-marking affixes. we then extended this list with genderneutral variants, presenting a catalogue of 692 gender-exclusive vs. -inclusive pairs, which we make available for future research. we further performed fine-tuning experiments on three llms. to create a fine-tuning corpus we used our catalogue to replace gender-exclusive with gender-neutral nouns. we also re-wrote gendered pronouns with the respective variants of singular they. fine-tuning with gender-neutral data showed an overall reduction in gender stereotyping as measured by likelihood of gendered word generation in stereotyped settings, as well as a reduction in the generation of harmful language when prompted with sentences containing words related to binary gender as well as the lgbtq+ community. however, we also showed that optimal bias reduction is dependent on model architecture and number of fine-tuning epochs, which need to be considered in deployment. we hope that our work will inspire further research into the effects of gender-inclusive terminology within large language models.