# Do Language Models Know Their Slang? Queer Slang Understanding in User-Generated Content

arXiv: https://arxiv.org/abs/2608.04847 · HTML: https://mlsanity.com/paper/2608.04847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [SLANG: New Concept Comprehension of Large Language Models](https://mlsanity.com/paper/2401.12585/) (2024)
- [Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias](https://mlsanity.com/paper/2406.00020/) (2024)
- [Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs](https://mlsanity.com/paper/2505.05660/) (2025)
- [Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models](https://mlsanity.com/paper/2405.08477/) (2024)
- [Agree to Disagree? A Meta-Evaluation of LLM Misgendering](https://mlsanity.com/paper/2504.17075/) (2025)
- [Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech](https://mlsanity.com/paper/2502.09004/) (2025)
- [Unequal Voices: How LLMs Construct Constrained Queer Narratives](https://mlsanity.com/paper/2507.15585/) (2025)
- [Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context](https://mlsanity.com/paper/2512.03671/) (2025)
- [QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Tasks](https://mlsanity.com/paper/2601.20731/) (2026)
- [Queer NLP: A Critical Survey on Literature Gaps, Biases and Trends](https://mlsanity.com/paper/2602.16151/) (2026)

## Abstract (arXiv)

Despite its cultural relevance and diffusion, queer slang remains underrepresented in Natural Language Processing research. Towards addressing this gap, we introduce Slang-Q, a manually curated dataset of naturally user-generated English sentences paired with queer slang terms and reference definitions, built upon a newly constructed taxonomy of 118 queer terms. We use this resource to conduct a first exploratory evaluation of language models on their ability to understand and define queer slang under varying prompting conditions. Slang-Q is intended as a basis for studying how current models handle sensitive, community-specific language and whether they can provide accurate and reliable information about such forms of identity and linguistic expression.