Harnessing Business and Media Insights with Large Language Models
Jan 1, 2024·,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,·
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Yujia Bao
Ankit Parag Shah
Neeru Narang
Jonathan Rivers
Rajeev Maksey
Lan Guan
Louise N. Barrere
Shelley Evenson
Rahul Basole
Connie Miao
Ankit Mehta
Fabien Boulay
Su Min Park
Natalie E. Pearson
Eldhose Joy
Tiger He
Sumiran Thakur
Koustav Ghosal
Josh On
Phoebe Morrison
Tim Major
Eva Siqi Wang
Gina Escobar
Jiaheng Wei
Tharindu Cyril Weerasooriya
Queena Song
Daria Lashkevich
Clare Chen
Gyuhak Kim
Dengpan Yin
Don Hejna
Mo Nomeli
Wei Wei
Abstract
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.
Type
Publication
arXiv