FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.
InInternational Conference on Machine Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CE 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.