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New system FinEvolveKG improves financial knowledge graph construction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FinEvolveKG, a new system for constructing financial knowledge graphs from SEC 10-Ks. This system separates general and persona-specific prompt blocks, using a reflective optimizer called GEPA that is confined to the persona block. This approach ensures that core rules remain stable while allowing for targeted prompt evolution, leading to improved performance and reduced variance compared to unconstrained optimization methods. Audits indicate that the generated triplets can serve as ground truth and are favored over those from unconstrained methods. AI

IMPACT This research introduces a novel method for prompt optimization in knowledge graph construction, potentially improving efficiency and accuracy in financial data analysis.

RANK_REASON The item is an academic paper detailing a new method for knowledge graph construction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New system FinEvolveKG improves financial knowledge graph construction

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The item is an academic paper detailing a new method for knowledge graph construction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Abhinav Arun, Jayita Bhattacharyya, Fabrizio Dimino, Bhaskarjit Sarmah, Stefano Pasquali ·

    FinEvolveKG: Controlled and Attributable Prompt Evolution for Financial Knowledge Graph Construction

    arXiv:2611.08643v1 Announce Type: new Abstract: Financial institutions construct knowledge graphs (KGs) from shared SEC 10-Ks to serve risk analysis, portfolio management, and investment research, whose analytical needs differ. Extraction must adapt without fragmenting shared gro…

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