Sarthak Mahajan supervised by Dr. Nimmi Rangaswamy received his  Master of Science –  Dual Degree in Computer Science and Engineering  (CSD). Here’s a summary of his research work on  Extreme Speech Classification in the Era of LLMs: Exploring Open-Source and Proprietary Models:

In recent years, the widespread adoption of the internet and the growth in user-base of various social media platforms have led to a notable increase in the proliferation of extreme speech online. While traditional Natural Language Processing (NLP) models have demonstrated proficiency in distinguishing between neutral text and non-neutral text (i.e. extreme speech), categorizing the diverse types of extreme speech presents significant challenges. The task of extreme speech classification is particularly nuanced, as it requires a deep understanding of socio-cultural contexts to accurately interpret the intent of the language used by the speaker. Even human annotators often disagree on the appropriate classification of such content, emphasizing the complex and subjective nature of this task. The use of human moderators also presents a scaling issue, necessitating the need for automated systems for extreme speech classification. The recent launch of ChatGPT has drawn global attention to the potential applications of Large Language Models (LLMs) across a diverse array of tasks. Due to their training on vast and diverse corpora, coupled with their demonstrated ability to capture and encode contextual information effectively, LLMs emerge as highly promising tools for tackling this specific task of extreme speech classification. Their capacity to generalize across linguistic patterns and contextual nuances makes them particularly well-suited for applications requiring sophisticated language understanding. LLMs have the capability to understand complex language and context, making them useful tools for identifying and controlling extreme speech on online platforms. In this thesis, we leverage the Indian subset of the extreme speech dataset (XTREME SPEECH) introduced by Maronikolakis et al. [55] to evaluate zero-shot classification, fine tuning, preference optimization, and ensemble methods. We address two primary research questions: (1) How do LLMs perform on multilingual extreme speech datasets, particularly within India’s linguistically and culturally diverse context? (2) To what extent can open-source LLMs serve as viable alternatives to proprietary GPT models, and does fine-tuning bridge any performance gap? Our findings indicate that while pre-trained LLMs demonstrate moderate efficacy in detecting extreme speech in a zero-shot setting, their performance is substantially enhanced through fine-tuning with domain-specific data. This improvement underscores the adaptability of these  models in capturing linguistic subtleties and contextual nuances. Furthermore, we note that while GPT-based models exhibit superior performance compared to open-source Llama models in a zero-shot context, the performance gap vanishes after fine-tuning. This suggests that open-source LLMs, when appropriately fine-tuned, can achieve comparable results, thereby presenting a viable alternative for extreme speech classification tasks.

July 2025