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.
| Comments: | 8 pages, appendices, GEM, ACL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2509.09544 [cs.CL] |
| (or arXiv:2509.09544v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.09544 arXiv-issued DOI via DataCite |
Submission history
From: Enrico Santus [view email]
[v1]
Thu, 11 Sep 2025 15:37:56 UTC (2,006 KB)
[v2]
Tue, 12 May 2026 21:35:27 UTC (2,607 KB)