【摘要】 金融行业长期以来面临海量市场数据与信息的处理难题。当前，大语言模型在通用文本理解任务上取得了显著进展，但在专业性更强的中文金融领域还有较大的提升空间。针对当前大语言模型在处理专业领域文本任务上的不足，提出基于知识增强的继续预训练和监督微调的两阶段训练方法，并改进了训练数据的组织形式和训练范式，从而提升模型在复杂金融场景下的性能。最后，通过实验验证了提出的知识增强方法在大模型训练中的有效性。更多还原

【Abstract】 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.更多还原