WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
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Abstract (arXiv)
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data are publicly available on Github and Huggingface.
Key facts. Cited by 117 papers in the map (data through 7 Oct 2026). Builds on 22 of its 60 references that are in the map. First posted on arXiv 31 Oct 2022.
Builds on
22 of 60 references.
References · 22
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Citing papers per year: 2023: 32; 2024: 40; 2025: 27; 2026: 18.
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