mlsanityData to 7 Oct 2026

Harnessing Business and Media Insights with Large Language Models

arXiv 2406.06559 ↗First posted 2 Jun 2024cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 63 of its 77 references that are in the map. First posted on arXiv 2 Jun 2024.

Builds on

63 of 77 references.

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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024LLM Unlearning via Loss Adjustment with Only Forget Datacited by 71
  2. 2025Exploring Criteria of Loss Reweighting to Enhance LLM Unlearningcited by 39
  3. 2025Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Modelscited by 27
  4. 2025GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detectioncited by 17
  5. 2025Label Smoothing Improves Gradient Ascent in LLM Unlearningcited by 3
  6. 2026STaR: Sensitive Trajectory Regulation for Unlearning in Large Reasoning Modelscited by 2
  7. 2024Enhancing Journalism with AI: A Study of Contextualized Image Captioning for News Articles using LLMs and LMMscited by 1