Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912646543966208 |
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| author | Tatarinov, Nikita Sukhani, Siddhant Shah, Agam Chava, Sudheer |
| author_facet | Tatarinov, Nikita Sukhani, Siddhant Shah, Agam Chava, Sudheer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07274 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities Tatarinov, Nikita Sukhani, Siddhant Shah, Agam Chava, Sudheer Computation and Language 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. |
| title | Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.07274 |