# How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages

arXiv: https://arxiv.org/abs/2509.15518 · HTML: https://mlsanity.com/paper/2509.15518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Pun Generation with Surprise](https://mlsanity.com/paper/1904.06828/) (2019)
- [GenQA: Generating Millions of Instructions from a Handful of Prompts](https://mlsanity.com/paper/2406.10323/) (2024)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [Learning to Explain Non-Standard English Words and Phrases](https://mlsanity.com/paper/1709.09254/) (2017)
- [Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?](https://mlsanity.com/paper/2310.17774/) (2023)
- [A Computational Framework for Slang Generation](https://mlsanity.com/paper/2102.01826/) (2021)
- [Simple Models for Word Formation in English Slang](https://mlsanity.com/paper/1804.02596/) (2018)
- [Detection of Slang Words in e-Data using semi-Supervised Learning](https://mlsanity.com/paper/1702.04241/) (2015)
- [Word reuse and combination support efficient communication of emerging concepts](https://mlsanity.com/paper/2411.05379/) (2024)

## Abstract (arXiv)

Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems. Recent advances in large language models (LLMs), however, have made the problem more approachable. While LLM agents are becoming more widely applied to intermediary tasks such as slang detection and slang interpretation, their generalizability and reliability are heavily dependent on whether these models have captured structural knowledge about slang that align well with human attested slang usages. To answer this question, we contribute a systematic comparison between human and machine-generated slang usages. Our evaluative framework focuses on three core aspects: 1) Characteristics of the usages that reflect systematic biases in how machines perceive slang, 2) Creativity reflected by both lexical coinages and word reuses employed by the slang usages, and 3) Informativeness of the slang usages when used as gold-standard examples for model distillation. By comparing human-attested slang usages from the Online Slang Dictionary (OSD) and slang generated by GPT-4o and Llama-3, we find significant biases in how LLMs perceive slang. Our results suggest that while LLMs have captured significant knowledge about the creative aspects of slang, such knowledge does not align with humans sufficiently to enable LLMs for extrapolative tasks such as linguistic analyses.