IIITDWD -zk@ D ravidian L ang T ech-2024: Leveraging the Power of Language Models for Hate Speech Detection in T elugu- E nglish Code-Mixed Text (2024)

Abstract: Hateful online content is a growing concern, especially for young people. While social media platforms aim to connect us, they can also become breeding grounds for negativity and harmful language. This study tackles this issue by proposing a novel framework called HOLD-Z, specifically designed to detect hate and offensive comments in Telugu-English code-mixed social media content. HOLD-Z leverages a combination of approaches, including three powerful models: LSTM architecture, Zypher, and openchat_3.5. The study highlights the effectiveness of prompt engineering and Quantized Low-Rank Adaptation (QLoRA) in boosting performance. Notably, HOLD-Z secured the 9th place in the prestigious HOLD-Telugu DravidianLangTech@EACL-2024 shared task, showcasing its potential for tackling the complexities of hate and offensive comment classification.

## methodology this section provides a comprehensive overview of the proposed hold-z framework. initially, a brief introduction to the problem statement and dataset is presented. subsequently, the approaches employed to address this challenge are discussed. ## results in this section we give extensive study results conducted on different models and different approaches to the problem statement that involves context focused approach and 7 billion-parameter models (7b's).