LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback

MCML Authors

Alexander Fraser

Prof. Dr.

Principal Investigator

Data Analytics & Statistics

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.

inproceedings

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COLING 2025

The 31st International Conference on Computational Linguistics. Abu Dhabi, United Arab Emirates, Jan 19-24, 2025.

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Authors

Y. Zhang • V. Hangya • A. Fraser

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Links

URL

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Research Area

 B2 | Natural Language Processing