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

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Authors: Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, Enrico Santus
Cited by: 1

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.

Fields of Study & Subjects

Stock Market Forecasting Methods Advanced Text Analysis Techniques Topic Modeling

Publication details

Journal / source
Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
Publisher
Association for Computational Linguistics
Pages
848–861
Publication date
1 January 2026