With the rapid development of LLMs, the evaluation of factual consistency between source documents and generated texts plays a more crucial role in natural language generation (NLG). Recent methods usually suffer from low quality and insufficient quantity of training data. In this paper, we propose a method for synthesizing factual consistency data by harnessing the vast knowledge stored within large language models. The synthetic data produced through this approach demonstrates notable discriminative abilities and robustness for training the factual consistency evaluation model. We adopt experiments on two benchmark datasets (TRUE and SummaC) and our method achieves 3.5% and 4.6% relative improvement on AUC-ROC metric respectively.

Recently, the development of foundation models has led to significant advances in the ability of artificial intelligence (AI) to generate multimodal content such as text and images. However, specialized industrial scenarios such as finance, which require high levels of security and compliance, pose challenges for the application of generative AI due to its uncontrollability. To address this issue, we propose FinGuard, a multimodal AI-generated content (AIGC) guardrail specifically designed for financial scenarios. We provide detailed definitions of the general quality, financial compliance, and security dimensions of AIGC, and implement the evaluation and inspection of multimodal AIGC including text and images. Our proposed FinGuard has been applied to a financial marketing application serving hundreds of millions of users.