Sequential Learning for Claim Verification and Explanation Generation in Financial Domains
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.