FANAL -- Financial Activity News Alerting Language Modeling Framework

Autor:	Patel, Urjitkumar, Yeh, Fang-Chun, Gondhalekar, Chinmay, Nalluri, Hari
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning 68T50 68T07 (Primary) 03B65 91G15 91F20 (Secondary) I.2.7 I.2.1 I.5.1 I.5.2 I.5.4 H.3.3
Druh dokumentu:	Working Paper
Popis:	In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time financial event detection and analysis, categorizing news into twelve distinct financial categories. FANAL leverages silver-labeled data processed through XGBoost and employs advanced fine-tuning techniques, alongside ORBERT (Odds Ratio BERT), a novel variant of BERT fine-tuned with ORPO (Odds Ratio Preference Optimization) for superior class-wise probability calibration and alignment with financial event relevance. We evaluate FANAL's performance against leading large language models, including GPT-4o, Llama-3.1 8B, and Phi-3, demonstrating its superior accuracy and cost efficiency. This framework sets a new standard for financial intelligence and responsiveness, significantly outstripping existing models in both performance and affordability. Comment: Accepted for the IEEE International Workshop on Large Language Models for Finance, 2024. This is a preprint version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.03527 Zobrazit plný text záznamu View this record from Arxiv