Summary of Harnessing Business and Media Insights with Large Language Models, by Yujia Bao et al.
Harnessing Business and Media Insights with Large Language Models
by 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
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces Fortune Analytics Language Model (FALM), a groundbreaking language model that provides users with direct access to comprehensive business analysis. Unlike generic language models, 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 leverage natural language queries to visualize financial data, generating insightful charts and graphs to understand trends across diverse business sectors. FALM employs three novel methods: time-aware reasoning for accurate event registration, thematic trend analysis for topic evolution insights, and content referencing and task decomposition for answer fidelity and visualization accuracy. The paper demonstrates significant performance improvements over baseline methods through automated and human evaluations, prioritizing responsible AI practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research introduces a new language model called Fortune Analytics Language Model (FALM). FALM helps people make better business decisions by providing them with in-depth information about companies and market trends. Unlike other language models, FALM uses a special knowledge base that is built from professional news articles. This allows it to give very accurate answers to complex business questions. Users can also ask FALM to create charts and graphs to help visualize financial data. The researchers have developed three new ways for FALM to work: one helps keep track of events accurately, another shows how topics change over time, and the third makes sure the information is reliable and accurate. The paper shows that FALM performs much better than other models do. |
Keywords
» Artificial intelligence » Knowledge base » Language model
