梳理金融领域语言模型研究，指明未来方向与数据机遇。
Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities
- 系统分析374篇金融NLP论文，聚焦221篇核心研究。
- 发现预测任务、评估指标、多语言数据等四大提升空间。
- 适合关注金融AI的学者与产业界人士参考。
近年来，语言模型在顶级自然语言处理会议中催生了大量金融相关研究。为系统考察这一趋势，本文回顾了2017至2024年间在38个会议与研讨会发表的374篇NLP论文，重点分析其中221篇直接涉及金融任务的研究。我们从11个定量与定性维度评估这些工作，识别出四大研究机遇：（i）拓展预测任务范围；（ii）引入更契合金融场景的评估指标；（iii）利用多语言及危机时期数据集；（iv）平衡预训练语言模型与高效或可解释的替代方案。研究提出具体可操作方向，并配套推荐数据集与工具，对学术界与产业界均有启示。
原文摘要 · Abstract (English)
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.
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