MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025) 文章

ArXiv CS.CL2026-05-27NEWSen作者: Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, Enrico Santus

摘要

arXiv:2509.09544v3 Announce Type: replace Abstract: Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge graphs from scientific corpora using ontology-guided LLM extraction to enable structured, large-scale trend analysis. Applied to 681 papers on GenAI in Finance (2022-2025), MetaGraph reveals three phases: early LLM-driven expansion of tasks and datasets, growing emphasis on limitations and risk, and a shift toward modular, system-oriented methods (e.g., retrieval-augmented designs). We release the resulting resource and artifacts to support reproducible meta-analysis and future monitoring of the field.