Guardrails, not Guidance: Understanding Responses to LGBTQ + Language in Large Language Models (2025)

Abstract: Language models have integrated themselves into many aspects of digital life, shaping everything from social media to translation. This paper investigates how large language models (LLMs) respond to LGBTQ+ slang and heteronormative language. Through two experiments, the study assesses the emotional content and the impact of queer slang on responses from models including GPT-3.5, GPT-4o, Llama2, Llama3, Gemma and Mistral. The findings reveal that heteronormative prompts can trigger safety mechanisms, leading to neutral or corrective responses, while LGBTQ+ slang elicits more negative emotions. These insights punctuate the need to provide equitable outcomes for minority slangs and argots, in addition to eliminating explicit bigotry from language models.

## introduction bias in language reflects and reinforces social norms, shaping perceptions of identity and inclusivity in both human-and machine-mediated communication. as large language models increasingly mediate our conversations, the biases encoded in these systems gain the power to construct and perpetuate inequities @xcite . queer communities, in particular, are heavily impacted by biased language technologies. online spaces often serve as vital forums for connection, support, and expression for lgbtq+ people, disproportionately exposing them to any potential llm bias @xcite . this paper examines biases in the responses of language technologies to two distinct kinds of linguistic expression related to the queer community: heteronormative language, and queer slang. understanding these biases is essential to ensure that these systems support fairness and inclusion, rather than amplifying existing inequities. recent research has highlighted the pervasive biases encoded in llms. this includes reinforcing harmful stereotypes, such as associating particular occupations with particular genders or disproportionately flagging minority dialects as toxic @xcite . benchmarks like winoqueer have shed light on anti-queer biases in model outputs, calling for community-driven evaluations to improve fairness @xcite . although efforts to mitigate bias have focused on safety mechanisms and debiasing techniques, these approaches primarily address overt discrimination and fail to account for subtler forms of bias, such as those found in responses to non-standard linguistic features like queer slang @xcite . this work builds on prior research in two clear ways. primarily, we focus on prompts containing language used by queer people, rather than queer topics or scenarios explicitly involving queer themes. additionally, we measure the emotional content of model responses, providing a more nuanced view into implicit bias shown by language models. together, this approach allows us to move beyond surface-level evaluations of fairness by examining how language models react to subtle linguistic markers of identity. in particular, this paper addresses gaps in the current research by focusing on two central questions: the findings presented here reveal critical gaps in current fairness approaches. while safety mechanisms neutralize bias in responding to overt heteronormative prompts, they fail to address systemic biases in responses to queer slang, which often elicit disproportionately negative emotional labels. these results highlight the limitations of existing debiasing efforts and underscore the importance of improving llm outputs for language used by marginalized communities. to foster truly inclusive nlp systems, future research and development must prioritize the equitable representation of minority linguistic forms. by expanding evaluation frameworks to account for nuanced biases, we can ensure that llms reflect the diversity of human language and support marginalized voices in digital spaces. the primary contributions of this work are: ## conclusion we highlight persistent biases in how llms interpret lgbtq+ slang and respond to heteronormative versus non-heteronormative prompts, revealing that while safety mechanisms mitigate overt biases, they fail to account for subtler linguistic discrimination. the findings show that llms often produce neutral or guarded responses to heteronormative prompts but disproportionately negative emotional labels for lgbtq+ slang, underscoring the models' difficulty in fairly processing community-specific language. as llms become integral to communication, addressing these biases will be crucial to foster a more inclusive digital landscape where all linguistic identities, particularly those of historically marginalized groups, are equitably represented and understood.