Research on knowledge-augmented Chinese financial large language model - CHENG Dawei; JIA Renjun; LI Jiangtong; DING Zhijun; JIANG Changjun (大数据)
The financial industry has long faced challenges in processing vast amounts of market data and information. Currently, large language models have made significant progress in general text understanding tasks, but there is still considerable room for improvement in more specialized domains, such as Chinese finance. To address the limitations of current large language models in handling professional domain-specific text tasks, a two-stage training approach based on finance knowledge-enhanced continued pre-training and supervised fine-tuning is designed. This approach improves the organization of training data and the training paradigm, thereby enhancing the model's capabilities in complex financial scenarios. Finally, experiments have validated the effectiveness of the proposed knowledge-enhanced approach in large model training.