LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback
Yaqi Zhang, Viktor Hangya, Alexander Fraser
Abstract
The capacity of large language models (LLMs) to understand and distinguish socially unacceptable texts enables them to play a promising role in abusive language detection. However, various factors can affect their sensitivity. In this work, we test whether LLMs have an unintended bias in abusive language detection, i.e., whether they predict more or less of a given abusive class than expected in zero-shot settings. Our results show that instruction-tuned LLMs tend to under-predict positive classes, since datasets used for tuning are dominated by the negative class. On the contrary, models fine-tuned with human feedback tend to be overly sensitive. In an exploratory approach to mitigate these issues, we show that label frequency in the prompt helps with the significant over-prediction.
BibTeX
@inproceedings{zhang-etal-2025-llm,
title = "{LLM} Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback",
author = "Zhang, Yaqi and
Hangya, Viktor and
Fraser, Alexander",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.188/",
pages = "2765--2780"
}