SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains

Autor:	Purbey, Jebish, Gupta, Siddhant, Manali, Nikhil, Pullakhandam, Siddartha, Sharma, Drishti, Srivastava, Ashay, Kadiyala, Ram Mohan Rao
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Computational Engineering Finance and Science Computer Science - Machine Learning Quantitative Finance - Computational Finance
Druh dokumentu:	Working Paper
Popis:	This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but also generating coherent, and concise explanations that clarify the rationale behind the classifications. Our approach achieved competitive results with an F1-score of 0.8283 for classification, and ROUGE-1 of 0.7253 for explanations. This work highlights the transformative potential of LLMs in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency while identifying areas for future improvement in robustness and domain adaptation. Comment: 6 pages, 9 figures, Submitted to FinNLP-FNP-LLMFinLegal @ COLING 2025
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.00549 Zobrazit plný text záznamu View this record from Arxiv