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

• URL: http://arxiv.org/abs/2310.18964v3
• Date: Sat, 30 Nov 2024 02:56:48 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 and appropriate benchmarking practices.
• Score: 10.014248704653
• License:
• 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. Ordinary least squares analyses suggest that the advantage of training with fine-grained hate speech labels is 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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