This paper introduces a zero-shot hate detection experiment using a multimodal large model. Although the implemented model comprises an unsupervised method, results demonstrate that its performance is comparable to previous supervised methods. Furthemore, this study proposed experiments with various prompts and demonstrated that simpler prompts, as opposed to the commonly used detailed prompts in large language models, led to better performance for multimodal hate speech event detection tasks. While supervised methods offer high performance, they require significant computational resources for training, and the approach proposed here can mitigate this issue.The code is publicly available at https://github.com/yamagishi0824/zeroshot-hate-detect.

In this paper, we present our approach to addressing the binary classification tasks, Tasks 5 and 6, as part of the Social Media Mining for Health (SMM4H) text classification challenge. Both tasks involved working with imbalanced datasets that featured a scarcity of positive examples. To mitigate this imbalance, we employed a Large Language Model to generate synthetic texts with positive labels, aiming to augment the training data for our text classification models. Unfortunately, this method did not significantly improve model performance. Through clustering analysis using text embeddings, we discovered that the generated texts significantly lacked diversity compared to the raw data. This finding highlights the challenges of using synthetic text generation for enhancing model efficacy in real-world applications, specifically in the context of health-related social media data.