Dehumanization of LGBTQ + Groups in Sexual Interactions with C hat GPT (2025)

Abstract: Given the widespread use of LLM-powered conversational agents such as ChatGPT, analyzing the ways people interact with them could provide valuable insights into human behavior. Prior work has shown that these agents are sometimes used in sexual contexts, such as to obtain advice, to role-play as sexual companions, or to generate erotica. While LGBTQ+ acceptance has increased in recent years, dehumanizing practices against minorities continue to prevail. In this paper, we hone in on this and perform an analysis of dehumanizing tendencies toward LGBTQ+ individuals by human users in their sexual interactions with ChatGPT. Through a series of experiments that model various concept vectors associated with distinct shades of dehumanization, we find evidence of the reproduction of harmful stereotypes. However, many user prompts lack indications of dehumanization, suggesting that the use of these agents is a complex and nuanced issue which warrants further investigation.

## introduction large language models (llms) have undoubtedly changed the way people interact with their computing hardware and software. these tools have seen widespread adoption in recent months to help write research papers @xcite , improve the individual journaling experience for better mental health @xcite and help with creative writing tasks @xcite , among other activities. therefore, examining human interactions with llm-powered conversational agents such as chatgpt may provide valuable insights into human behavior @xcite . sometimes the use cases of llm-powered conversational agents are sexual. for example, they are used to obtain sexual information and advice, as romantic or sexual companions, and to generate erotica and pornography @xcite . as evidence of this, @xcite labeled a sample of 5k user-generated chatgpt prompts, reporting that 6.3% of the inputs contained sexual and erotic content. past work has analyzed porn consumption as a means to study sexual behavior @xcite . doing so can evade the limitations of more traditional sexual studies such as experimentation, which demands time-consuming planning to comply with ethical guidelines @xcite . like studying porn, analyzing sexual conversations between users and conversational agents may be a viable way to study sexual behavior. because this type of study doesn't require deploying surveys-the traditional method for studying porn consumption-it may surpass the advantages of studying porn in terms of scalability and ease of study. it also has the added advantage of rich textual data exemplifying the ways people think and talk about sex. this type of sexual experience will be more tailored to the user's particular wants than porn, and therefore potentially more informative to study. it has long been recognized that, though porn can serve as a safe space for the queer community (flory, 2024), the representation of lgbtq+ people in mainstream porn can be problematic @xcite . for example, transgender people may be dehumanized by being overly-objectified @xcite . lesbian representation in mainstream porn largely caters to the male gaze, leading to fetishization @xcite @xcite . though generally thought of as less problematic, male gay porn has been critiqued for reinforcing homophobia @xcite . like porn, erotic conversational agents have their advantages for queer populations, such as providing emotional support @xcite . however, we seek to understand whether-in spite of these positive effects-problematic representations of lgbtq+ people observed in porn can also be found in user prompts with conversational agents. to this end, the main contributions of this work include: (1) an analysis of the way users represent lgbtq+ people in sexual interactions with chat-gpt using an extended version of @xcite 's pre-existing framework for identifying dehumanizing language and (2) a discussion of our findings that motivates further exploration of sexual interactions with llm-powered conversational agents. ## related work in this section we cover the background on llms and sexuality, discuss nlp for queer sociolinguistics and cover prior dehumanization work. ## data we conduct our analysis on a filtered portion of the wildchat-1m-full dataset @xcite ), 1 which contains 1 million conversations between users and openai's gpt-3.5 and 4. we filter the dataset to only include conversations in english which have been marked by the included openai moderation results as containing sexual content. because we are interested in studying how humans characterize those from lgbtq+ communities (rather than how llms do), we remove all system responses from the set. the resulting dataset contains approximately 38 thousand unique user turns from sexual conversations. to identify mentions of various groups, we build a lexicon based on the textual identity detection and augmentation lexicon (tidal), foot_1 a dataset formed to enable automatic detection of identity labels @xcite . it has coverage for identity groups including race, nationality, ethnicity, sexual orientation, gender identity, gender expression, sex characteristics and religion; it includes slurs. because our vector approaches (discussed in section 4) rely on word embeddings, we only consider single-word nouns. based on these words, we formed lexicons to identify mentions for seven groups: lgbtq+, lgb, transgender, gay men, lesbian, bisexual, and heterosexual. word lists for each group are included in appendix a.1. ## measuring dehumanization mendelsohn et al. (2020) present a structure for analyzing dehumanization in media. they outline several elements of dehumanization from prior social psychology literature including: a) negative evaluation of a target group, b) moral disgust, and c) vermin as a dehumanizing metaphor. we follow their framework for analyzing each element and add an additional component: d) objectification. ## dehumanization of lgbtq+ people in wildchat in our analysis of the dehumanization of lgbtq+ people in user prompts in the sexual subset of the wildchat dataset, we unveil a number of interesting insights which point to diverse portrayals of lgbtq+ groups in human interactions with llmpowered agents. lgbtq+ people are objectified less than "neutral" terms. in figure 1 , we find evidence that lgbtq+ terms are used in less similar contexts to object words than the predetermined "neutral" terms. the vermin metaphor results (shown in figure 6 ), show that all lgbtq+ word groupsw are, on average, used in less similar context to vermin words than the "neutral" terms. in figure 7 , we find that some of the group vectors have a smaller cosine distance to the "moral disgust" concept vector, while others (including "bisexual" and "gay men") have slightly longer distances. collectively, these results seem to indicate that these groups are characterized with similar levels of, or less, dehumanizing language than the terms person, people, individual, and individuals. dehumanization is relatively similar across groups. the "vermin", "object", and "moral disgust" vectors exhibit similar cosine distances to the vectors associated with each subgroup in the lgbtq+ acronym. while some groups may be dehumanized more, the differences are not stark. this indicates that on average, when people ask chat-gpt to generate text with lgbtq+-related erotic content, they tend to dehumanize every subgroup in a similar measure. however, some terms, such as "twink" and "faggot", exhibit the longest cosine distance from the moral disgust concept vector when compared to all the other terms in the lgbtq+ category, indicating a semantic shift in these terms. one possible reason for this is that societal acceptance of gay men has led some chatgpt users to view them in a more positive and sexually desirable way. the same cannot be said for other lgbtq+ groups. transgender and lesbian populations are dehumanized more than other groups. in line with patterns in mainstream porn, we find that lesbian and transgender people are stripped of human qualities more often than other groups. in figure 7 we observe that the "transgender" group vector is more closely related to the moral disgust concept vector than any other group vector. the "lesbian" group vector is also situated nearer the "moral disgust" concept vector than the "neutral" vector. these groups also face more negative evaluations, as shown in figure 2 . outdated terms are used in more dehumanizing contexts. in line with @xcite 's findings in their analysis of dehumanization in u.s. news media, outdated terms used to refer to people from the lgbtq+ community are used in more dehumanizing ways than other terms, such. for example, the terms "homosexual" and "hermaphrodite" are generally considered to be outdated and offensive. foot_4 . these words are more closely associated with the vermin, object, and moral disgust concept vectors than other lgbtq+ terms (figures 3 , 4 , and 8). ## conclusion motivated by our findings, we call attention to further studying how conversational agents facilitate or hinder marginalized groups' representation in sexual contexts. we propose extending this work to explore representation across lines of race and gender. our findings also suggest that it may be informative to expand our focus past dehumanizing language to study other possible modes of representation. by understanding how people reproduce harmful stereotypes-or not-in their prompting practices of erotic text, we could develop safeguards to minimize the dehumanization of the lgbtq+ community, while promoting the use of these tools among lgbtq+ individuals who cannot find proper representation of their desires in traditional media.