“自动报告生成技术在提高工作效率和节约人力资源方面具有显著潜力。大语言模型的出现使得报告流畅度与可解释性得到提升。然而,现有工作仍依赖人工,缺乏灵活性和丰富度。同时,小模型错误或冗余的输出与大模型自身的随机性会导致报告质量不稳定。本文提出大小模型协同的自动报告生成框架AutoRG,通过大模型的工具理解与规划能力减少人工干预,提升报告丰富度,并通过信息修正与报告迭代机制提高报告的稳定性。本文以自动专利报告生成为场景,从多个维度对AutoRG进行全面测试。结果表明,该框架在提高报告生成的丰富度和质量稳定性方面具有显著优势。”

Classical Chinese poetry has a long history and is a precious cultural heritage of humankind. Displaying the classical Chinese poetry in a visual way, helps to cross cultural barriers in different countries, making it enjoyable for all the people. In this paper, we construct a multi-modal knowledge graph for classical Chinese poetry (PKG), in which the visual information of words in the poetry are incorporated. Then a multi-modal pre-training language model, PKG-Bert, is proposed to obtain the poetry representation with visual information, which bridges the semantic gap between different modalities. PKG-Bert achieves the state-of-the-art performance on the poetry-image retrieval task, showing the effectiveness of incorporating the multi-modal knowledge. The large-scale multi-modal knowledge graph of classical Chinese poetry will be released to promote the researches in classical Chinese culture area.