Adapting Psycholinguistic Research for LLM s: Gender-inclusive Language in a Coreference Context (2025)

Abstract: Gender-inclusive language is often used with the aim of ensuring that all individuals, regardless of gender, can be associated with certain concepts. While psycholinguistic studies have examined its effects in relation to human cognition, it remains unclear how Large Language Models (LLMs) process gender-inclusive language. Given that commercial LLMs are gaining an increasingly strong foothold in everyday applications, it is crucial to examine whether LLMs in fact interpret gender-inclusive language neutrally, because the language they generate has the potential to influence the language of their users. This study examines whether LLM-generated coreferent terms align with a given gender expression or reflect model biases. Adapting psycholinguistic methods from French to English and German, we find that in English, LLMs generally maintain the antecedent’s gender but exhibit underlying masculine bias. In German, this bias is much stronger, overriding all tested gender-neutralization strategies.

## introduction over the last few decades, activism by feminist linguists has led to increased use of gender-neutral or gender-fair wording, especially in grammatical gender languages such as french or german @xcite . the aim of these forms is to alleviate masculinedefault bias and establish representation for people with non-binary gender identities @xcite . psycholinguistic studies have shown that genderneutral alternatives can increase the visibility of women and non-binary people @xcite . as large language models (llms) are embedded into everyday systems and are used as writing assistants and content creators, the language they generate can have an impact on equal treatment and linguistic representation of women and non-binary people foot_0 . however, despite the fact that gender bias in nlp has been examined from many different angles @xcite , gender-inclusive language in the context of llms has only begun to be investigated @xcite . the processing of gender-inclusive vs. gendered language remains under-explored in english llms @xcite and, to our knowledge, entirely unexamined in german llms. to address this, we compare the processing of gendered and gender-inclusive language in both english, a notional gender language, and german, a grammatical gender language. we adapt a psycholinguistic study by @xcite to explore how the presence of masculine, feminine or neutral gender in one sentence influences (1) the likelihood of a reference to that gender in a subsequent sentence and (2) the gender mentioned in an llm-generated completion. we find that while english llms generally keep antecedent and coreferent gender consistent, they are unlikely to use they as a singular pronoun and contain underlying masculine bias. the german llm we tested showed a strong preference for masculine coreferents, regardless of the gender or gender-inclusive strategy used in the antecedent phrase. we also find evidence that german genderinclusive language strategies increase the probability of feminine and neutral gender. this finding encourages us to believe that the use of genderinclusive over generic masculine expressions in german llms has the potential to diversify gender representation. contributions this study translates psycholinguistic methodologies to llms, enabling com-parisons between human and model reasoning. it introduces a novel approach to assessing whether gender-inclusive expressions promote gender-neutral interpretations within llms foot_1 . additionally, it provides the first analysis of german gender-inclusive strategies in this context, showing that they partially achieve their intended effects by increasing associations with feminine and neutral gender, aligning with psycholinguistic findings. ## bias statement in this work, we define gender bias in an llm as the tendency to assign higher likelihoods to gendered linguistic forms when referring to an entity that was initially introduced in a gender-neutral way. this behavior can result in representational harms @xcite : specifically, if masculine forms are used to refer to previously introduced gender-neutral nouns which describe a group or person of unknown gender, women and non-binary people are excluded from representation. such linguistic erasure can reinforce their marginalization in society @xcite @xcite . ## background the field of feminist psycholinguistics is concerned with evaluating human biases related to language. studies have shown how masculine generics are in fact not interpreted generically @xcite , and that changing the language to be gender-inclusive also increases mental representation for women and non-binary people @xcite . the term genderinclusive language describes linguistic strategies and neologisms to eliminate male-as-norm bias (@xmath0, binary model of gender (husband/@xmath1. large language models (llms) have also been shown to exhibit various social biases, including gender bias @xcite . however, few studies ha explored the processing of genderinclusive language within llms. there are two main areas of investigation: gender-inclusive role nouns (fire fighter, chairperson, etc.) and genderneutral pronouns such as singular they. the present research addresses both. to investigate the processing of genderinclusive role nouns in llms, @xcite adapted a psycholinguistic study on sentence acceptability judgments and social attitudes for bert @xcite . they first calculated bert's relative probability of a given masculine, feminine or neutral role noun (e.g. fireman/firewoman/fire fighter) within a sentence context. bert's responses were then connected to the social attitudes of the human participants giving the same responses. the researchers found that bert aligned most with people who had moderate to conservative views. there are several studies examining genderneutral pronouns in llms. @xcite draw on psycholinguistic research into swedish neopronouns and adapted an eye-tracking study for llms. they demonstrated that while humans do not have trouble processing neopronouns @xcite , they are associated with greater processing difficulty in llms. correspondingly, models also have lower pronoun fidelity for feminine and singular they pronouns, meaning that they are less likely to use them even if they were introduced alongside a corresponding entity @xcite . when comparing an llm's processing of singular they in a generic sense vs. referring to a specific person, models have less trouble with generic they @xcite . in terms of social attitudes, bert's likelihood to generate singular they resembled the judgments of participants with low to moderate acceptance of non-binary gender @xcite . psycholinguistic studies that were previously adapted for llms, including the research this paper is based on, often contain anaphora. anaphora is defined "in a looser sense, [as] any relation in which something is understood in the light of what precedes it" @xcite . the preceding term is the antecedent, while the referring term is the coreferent. the resolution of this relationship, finding the corresponding antecedent for a coreferent, is a large research field within nlp. coreference resolution (cr) is is relevant for downstream nlp tasks such as named entity recognition, summarization or question answering @xcite . cr systems have previously been shown to exhibit gender bias, relying on stereotypes instead of syntactic information or real-world gender distributions @xcite . to evaluate cr systems for gender biases, challenge datasets based on the winograd schema @xcite were developed @xcite . these datasets contain instances in which a pronoun must be resolved to refer to one of two previously mentioned entities, such as in the sentence "the paramedic performed cpr on the passenger even though she/he/they knew it was too late." @xcite . while most challenge datasets contain a single sentence, and assess the resolution of singular pronouns, this research focuses on coreference between two different sentences in both singular and plural. in german, the issue of gender-inclusive language is more intricate than in english. german marks nouns, articles and adjectives for masculine, feminine or neuter gender, traditionally using masculine forms as the generic. similar to english, masculine generics have a predominantly masculine interpretation @xcite , which is also reflected in nlp models trained on german text @xcite . to increase women's visibility and/or take gender out of the equation, feminist scholars pushed for linguistic strategies to make role nouns more inclusive @xcite . in nlp, there have been efforts to automate the integration of these strategies into text @xcite , as well as research on gender-neutral machine translation into german @xcite . however, it is unclear how german gender-fair language is processed by an llm and we aim to provide some initial answers to this issue in this paper. ## methodology in order to uncover how llms process genderinclusive in contrast to gendered language, we adapted @xcite study design of sentence pairs containing antecedent and coreferent phrases ( §4.1). we used several llms ( §4.2) for our experiments on measuring the probability of specific gendered or gender-neutral terms ( §4.3) and analyzing the gender contained in model generations ( §4.4). ## results this section lays out the results for our experiments on coreferent probability and coreferent generation. for each of these, we will first present the english and then the german results. ## discussion both experiments on measuring coreferent probability and generation of coreferents demonstrated that generally, models tend to match coreferent gender to the antecedent gender. however, there are several caveats to this observation. for english models, whether or not the gender of the coreferent aligns with the antecedent depends on whether the sentences are singular or plural. our english coreferent probability experiments in the singular setting (figure 1b ) showed that when the antecedent is neutral, the masculine pronoun he has the highest probability instead of they, meaning that models struggle to interpret the pronoun they as a singular pronoun. this finding was also reported by @xcite . in language generation applications, this might contribute to the erasure of people of non-binary gender who use they/them pronouns, as well as reinforce male-as-norm biases when people of unknown gender are referenced with masculine pronouns @xcite . furthermore, in the english plural experiments the most probable coreferent gender generally follows the gender of the antecedent. however, the second-and third-highest gender probabilities paint a more nuanced picture (figure 1 ). for both feminine and neutral antecedents, masculine coreferents are second-most likely. this illustrates bias, because an equitable model would display similar probabilities for feminine and masculine coreferents given a gender-neutral antecedent. for feminine antecedents, it would also assign higher probabilities to neutral over masculine coreferents. thus, while the model prioritizes gendered context clues -a desirable behavior -it still exhibits an underlying masculine default bias. this masculine bias was not just underlying but clearly visible in our german experiments. measuring the probability of specific coreferents showed that männer 'men' always had a higher probability than either the feminine coreferent frauen 'women' or neutral coreferent personen 'persons'. this important finding shows that gender bias in the model outweighs information it received in the prompt, which might lead to a reinforcement of male-asnorm bias through a likely prevalence of masculine terms in the output. it is important to note, however, that the coreferent generation experiments for german did not show masculine bias to the same extent as the coreferent probability experiments. this might have been due to the model often simply repeating the antecedent phrase in the generations. in our coreferent probability experiments coreferent terms differed from the antecedent phrases. one encouraging finding from the german experiments is that, despite masculine gender having the highest probability, gender-inclusive strategies help increase the probability of feminine and neutral coreferents. this supports one of the aims of using gender-inclusive language: to allow equal association of all genders with respective terms. our findings clearly illustrate that the model we used does not show this equal association, however, it is promising that the use of gender-fair language can increase the probability of an association with gender-neutral and feminine terms. this finding mirrors the result of psycholinguistic studies into the effects of gender-inclusive language on humans @xcite . ## conclusion this research adapted @xcite 's psycholinguistic experiments on the effects on genderfair language on anaphora resolution to the domain of llms. we investigated how the use of gendered or gender-inclusive language within one sentence influences the generation of language in consecutive sentences. our findings indicate that while english llms are likely to continue to use the gender of a mentioned entity in a subsequent sentence, there is an underlying prevalence for masculine gender. for german, this bias appears more pronounced, with masculine gender always having the highest probability in spite of feminine or neutral gender information in the previous sentence. however, with reference to @xcite findings, gender-inclusive language strategies in german also increase the probability of feminine and gender-neutral referents. this research therefore supports the value of using gender-inclusive language in an llm context, especially in underrepresented languages like german.