LLMs

Accepted at Chipsal @ COLING 2025. This study addresses hate speech detection and target identification in Devanagari-scripted languages (Hindi, Marathi, Nepali, Bhojpuri, Sanskrit). Subtask B focuses on detecting hate speech, while Subtask C identifies specific targets, such as individuals or communities. The proposed MultilingualRobertaClass model, based on the ia-multilingual-transliterated-roberta transformer, uses contextualized embeddings for multilingual and transliterated contexts. It achieved 88.40% accuracy in Subtask B and 66.11% in Subtask C on the test set.

Accepted at FINLP-FNP-LLMFinLegal @ COLING 2025 | Secured 3rd position in the workshop. Our system for the COLING 2025 FMD challenge focused on detecting financial misinformation using large language models (Qwen, Mistral, Gemma-2) combined with pre-processing and sequential learning. It not only classified fraudulent content with an F1-score of 0.8283 but also generated clear explanations, achieving a ROUGE-1 score of 0.7253. This work demonstrates the potential of LLMs in combating financial misinformation, improving transparency, and highlights areas for future enhancements in robustness and domain adaptation.