LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection

• URL: http://arxiv.org/abs/2310.18964v2
• Date: Sat, 30 Mar 2024 15:01:08 GMT
• Title: LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection
• Authors: Ahmad Nasir, Aadish Sharma, Kokil Jaidka,
• Abstract summary: This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech. LLMs offer a huge advantage over the state-of-the-art even without pretraining. We conclude with a vision for the future of hate speech detection, emphasizing cross-domain generalizability.
• Score: 10.014248704653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the evolving landscape of online communication, hate speech detection remains a formidable challenge, further compounded by the diversity of digital platforms. This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech, to address two central questions: (1) To what extent does the model performance depend on the fine-tuning and training parameters?, (2) To what extent do models generalize to cross-domain hate speech detection? and (3) What are the specific features of the datasets or models that influence the generalization potential? The experiment shows that LLMs offer a huge advantage over the state-of-the-art even without pretraining. To answer (1) we analyze 36 in-domain classifiers comprising LLaMA, Vicuna, and their variations in pre-trained and fine-tuned states across nine publicly available datasets that span a wide range of platforms and discussion forums. To answer (2), we assessed the performance of 288 out-of-domain classifiers for a given end-domain dataset. In answer to (3), ordinary least squares analyses suggest that the advantage of training with fine-grained hate speech labels is greater for smaller training datasets but washed away with the increase in dataset size. We conclude with a vision for the future of hate speech detection, emphasizing cross-domain generalizability and appropriate benchmarking practices.

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