Efficient Multi-Expert Tabular Language Model for Banking

Pre-training large Tabular Language Models (TaLMs) on tabular data has shown effectiveness for table understanding tasks. However, training proprietary large TaLMs on a company's private databases requires substantial computational resources. This paper presents an efficient multi-expert TaLM architecture and training method tailored for multi-domain databases and modest infrastructure. This architecture leverages a divide-and-conquer pretraining approach and a sparsely activated fine-tuning paradigm to reduce computation. Using this architecture, we pre-train and fine-tune a TaLM with 10 billion parameters on a banking database under simple computational infrastructures. We apply our TaLM to support various important banking applications, including risk assessment, information prediction, and profit assessment. Compared with previous baselines, our model achieves +29.3% in [email protected]% on risk assessment and +16.5% in accuracy on information prediction, showing great effectiveness and profitability of our model. This model is successfully deployed in WeBank and now supports many real business scenarios.