Harnessing Business and Media Insights with Large Language Models
<|reference_start|>Harnessing Business and Media Insights with Large Language Models: 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.<|reference_end|>
@article{bao2024harnessing,
    title={Harnessing Business and Media Insights with Large Language Models},
    author={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},
    journal={arXiv preprint arXiv:2406.06559},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06559},
    primaryClass={cs.CL cs.AI cs.LG}
}