Harnessing Business and Media Insights with Large Language Models

التفاصيل البيبلوغرافية
العنوان: Harnessing Business and Media Insights with Large Language Models
المؤلفون: Bao, Yujia, Shah, Ankit Parag, Narang, Neeru, Rivers, Jonathan, Maksey, Rajeev, Guan, Lan, Barrere, Louise N., Evenson, Shelley, Basole, Rahul, Miao, Connie, Mehta, Ankit, Boulay, Fabien, Park, Su Min, Pearson, Natalie E., Joy, Eldhose, He, Tiger, Thakur, Sumiran, Ghosal, Koustav, On, Josh, Morrison, Phoebe, Major, Tim, Wang, Eva Siqi, Escobar, Gina, Wei, Jiaheng, Weerasooriya, Tharindu Cyril, Song, Queena, Lashkevich, Daria, Chen, Clare, Kim, Gyuhak, Yin, Dengpan, Hejna, Don, Nomeli, Mo, Wei, Wei
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
الوصف: This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performance metrics, and expert insights. Unlike generic LLMs, FALM leverages a curated knowledge base built from professional journalism, enabling it to deliver precise and in-depth answers to intricate business questions. Users can further leverage natural language queries to directly visualize financial data, generating insightful charts and graphs to understand trends across diverse business sectors clearly. FALM fosters user trust and ensures output accuracy through three novel methods: 1) Time-aware reasoning guarantees accurate event registration and prioritizes recent updates. 2) Thematic trend analysis explicitly examines topic evolution over time, providing insights into emerging business landscapes. 3) Content referencing and task decomposition enhance answer fidelity and data visualization accuracy. We conduct both automated and human evaluations, demonstrating FALM's significant performance improvements over baseline methods while prioritizing responsible AI practices. These benchmarks establish FALM as a cutting-edge LLM in the business and media domains, with exceptional accuracy and trustworthiness.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2406.06559
رقم الأكسشن: edsarx.2406.06559
قاعدة البيانات: arXiv