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

Paolo Pedinotti; Peter Baumann; Nathan Jessurun; Leslie Barrett; Enrico Santus

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

Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, Enrico Santus

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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.

Anthology ID:: 2026.gem-main.71
Volume:: Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
Month:: July
Year:: 2026
Address:: San Diego, California, USA
Editors:: Simon Mille, Sebastian Gehrmann, Patrícia Schmidtová, Ondřej Dušek, Marzieh Fadaee, Kyle Lo, Enrico Santus, Gabriel Stanovsky
Venues:: GEM | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 848–861
Language:
URL:: https://aclanthology.org/2026.gem-main.71/
DOI:
Bibkey:
Cite (ACL):: Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, and Enrico Santus. 2026. MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025). In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 848–861, San Diego, California, USA. Association for Computational Linguistics.
Cite (Informal):: MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025) (Pedinotti et al., GEM 2026)
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PDF:: https://aclanthology.org/2026.gem-main.71.pdf

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@inproceedings{pedinotti-etal-2026-metagraph,
    title = "{M}eta{G}raph: A Large-Scale Meta-Analysis of {G}en{AI} in Financial {NLP} (2022{--}2025)",
    author = "Pedinotti, Paolo  and
      Baumann, Peter  and
      Jessurun, Nathan  and
      Barrett, Leslie  and
      Santus, Enrico",
    editor = "Mille, Simon  and
      Gehrmann, Sebastian  and
      Schmidtov{\'a}, Patr{\'i}cia  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Fadaee, Marzieh  and
      Lo, Kyle  and
      Santus, Enrico  and
      Stanovsky, Gabriel",
    booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.gem-main.71/",
    pages = "848--861",
    ISBN = "979-8-89176-423-1",
    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."
}

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%0 Conference Proceedings
%T MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025)
%A Pedinotti, Paolo
%A Baumann, Peter
%A Jessurun, Nathan
%A Barrett, Leslie
%A Santus, Enrico
%Y Mille, Simon
%Y Gehrmann, Sebastian
%Y Schmidtová, Patrícia
%Y Dušek, Ondřej
%Y Fadaee, Marzieh
%Y Lo, Kyle
%Y Santus, Enrico
%Y Stanovsky, Gabriel
%S Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-423-1
%F pedinotti-etal-2026-metagraph
%X 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.
%U https://aclanthology.org/2026.gem-main.71/
%P 848-861

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Markdown (Informal)

[MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025)](https://aclanthology.org/2026.gem-main.71/) (Pedinotti et al., GEM 2026)

MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025) (Pedinotti et al., GEM 2026)

ACL

Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, and Enrico Santus. 2026. MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022–2025). In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 848–861, San Diego, California, USA. Association for Computational Linguistics.