Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities
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Abstract (arXiv)
Recent advances in language modeling have led to a growing number of papers related to finance in top-tier Natural Language Processing (NLP) venues. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 quantitative and qualitative dimensions, and our study identifies the following opportunities for NLP researchers: (i) expanding the scope of forecasting tasks; (ii) enriching evaluation with financial metrics; (iii) leveraging multilingual and crisis-period datasets; and (iv) balancing PLMs with efficient or interpretable alternatives. We identify actionable directions supported by dataset and tool recommendations, with implications for both the academia and industry communities.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 58 of its 200 references that are in the map. First posted on arXiv 9 Apr 2025.
Builds on
58 of 200 references.
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38 more
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