Harnessing Business and Media Insights with Large Language Models
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2024
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing Business and Media Insights with Large Language Models 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 Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Harnessing Business and Media Insights with Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.06559 |