Facilitating financial technology with the large-language model (LLM) has been developing in recent years. To address the challenges in one of the biggest world-wide markets, China, Chinese-expertise financial LLM has also been studied. The related works focus on conventional NLP tasks in finance, while developing LLM for specific tasks is also required. Besides, in the credit loan business, the existing AI-based approaches are largely related to Credit like Credit rating and Fraud prediction, while credit product customization is still missing. In China, Inclusive Finance and Rural Finance become two hot topics that raise critical challenges in flexibly customizing credit products to meet the variable fund requirements of small & micro businesses, individual businesses, and agricultural businesses of local character. In this paper, the credit product customization is studied by developing an LLM-based financial AI assistant for the credit loan business. It is proposed to satisfy the business requirements of customer counseling, recommendation, and question-answers regarding credit loans. The proposed LLM is developed by Chinese prompt data automatically constructed based on a small set of real-world credit products. The experiments demonstrate its effectiveness in credit loan-related ability while maintaining comparable performance in conventional finance NLP tasks.

Automatic generating the clinically accurate radiology report from X-ray images is important but challenging. The identification of multi-grained abnormal regions in image and corresponding abnormalities is difficult for data-driven neural models. In this work, we introduce a Memory-aligned Knowledge Graph (MaKG) of clinical abnormalities to better learn the visual patterns of abnormalities and their relationships by integrating it into a deep model architecture for the report generation. We carry out extensive experiments and show that the proposed MaKG deep model can improve the clinical accuracy of the generated reports.

Automatic generating financial report from a set of news is important but challenging. The financial reports is composed of key points of the news and corresponding inferring and reasoning from specialists in financial domain with professional knowledge. The challenges lie in the effective learning of the extra knowledge that is not well presented in the news, and the misalignment between topic of input news and output knowledge in target reports. In this work, we introduce a disentangled variational topic inference approach to learn two latent variables for news and report, respectively. We use a publicly available dataset to evaluate the proposed approach. The results demonstrate its effectiveness of enhancing the language informativeness and the topic accuracy of the generated financial reports.