Non Hate Speech

Non-hate speech research focuses on identifying and mitigating harmful online content, encompassing both textual and spoken forms across multiple languages. Current research employs deep learning models, such as Bi-LSTMs and BERT, often enhanced by techniques like contrastive learning and direct preference optimization, to improve detection accuracy and reduce biases in these models. This work is crucial for creating safer online environments and advancing the development of ethical and unbiased language processing technologies, with applications ranging from social media moderation to improving the fairness of large language models. The field is actively addressing challenges like data imbalance and the nuances of hate speech across different languages and cultures.