Open-domain multi-turn dialogue generation encounters the significant challenge of lacking various types of knowledge from diverse sources. Existing models typically focus on identifying specific types of dialogue knowledge and utilize corresponding datasets for training. However, this approach often leads to limited generalization capabilities and increased computational resource requirements. Recently, large language models (LLMs) have shown impressive performance on natural language processing tasks. To harness the knowledge storage of LLMs, we propose a framework named KnowEE that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and then exploits the obtained knowledge for response generation. Our framework comprises two phases: First, we leverage five external datasets encompassing various types of knowledge to extract the most relevant samples to the dialogue context which are served as prompts to generate corresponding type of knowledge; Second, we inject the acquired knowledge into the ongoing dialogue context in fine-grained and coarse-grained manners, which is then fed into LLMs to generate the final dialogue response. Both automatic and manual evaluation results validate the effectiveness of our framework in exploring and exploiting multi-source multi-type knowledge to generate coherent, informative, and fluent responses.

“开放式自动故事生成通过输入故事的开头、大纲、主线等,得到具有一致性、连贯性和逻辑性的故事。现有的方法想要提升生成故事的质量,往往需要大量训练数据和更多参数的模型。针对以上问题,该文利用提示学习在零样本与少样本场景下的优势,同时使用外部常识推理知识,提出了一种故事生成方法。该方法将故事生成分为三个阶段:输入故事的开头,常识推理模型生成可能的事件;根据类型不同,将事件填入问题模板中,构建引导模型生成合理回答的问题;问答模型产生对应问题的答案,并选择困惑度最小的作为故事下文。重复上述过程,最终生成完整的故事。自动评测与人工评测指标表明,与基线模型相比,该文提出的方法能够生成更连贯、具体和合乎逻辑的故事。”