Understanding Slang with LLMs: Modelling Cross-Cultural Nuances through Paraphrasing
Ifeoluwa Wuraola, Nina Dethlefs, Daniel Marciniak
Abstract
In the realm of social media discourse, the integration of slang enriches communication, reflecting the sociocultural identities of users. This study investigates the capability of large language models (LLMs) to paraphrase slang within climate-related tweets from Nigeria and the UK, with a focus on identifying emotional nuances. Using DistilRoBERTa as the base-line model, we observe its limited comprehension of slang. To improve cross-cultural understanding, we gauge the effectiveness of leading LLMs ChatGPT 4, Gemini, and LLaMA3 in slang paraphrasing. While ChatGPT 4 and Gemini demonstrate comparable effectiveness in slang paraphrasing, LLaMA3 shows less coverage, with all LLMs exhibiting limitations in coverage, especially of Nigerian slang. Our findings underscore the necessity for culturally sensitive LLM development in emotion classification, particularly in non-anglocentric regions.
BibTeX
@inproceedings{wuraola-etal-2024-understanding,
title = "Understanding Slang with {LLM}s: Modelling Cross-Cultural Nuances through Paraphrasing",
author = "Wuraola, Ifeoluwa and
Dethlefs, Nina and
Marciniak, Daniel",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.869/",
doi = "10.18653/v1/2024.emnlp-main.869",
pages = "15525--15531"
}