We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models. Compared to previous knowledge models, NovaCOMET allows open-format relations enabling direct application to reasoning tasks; compared to general task models like Flan-T5, it explicitly centers knowledge, enabling superior performance for commonsense reasoning. NovaCOMET leverages the knowledge of opaque proprietary models to create an open knowledge pipeline. First, knowledge is symbolically distilled into NovATOMIC, a publicly-released discrete knowledge graph which can be audited, critiqued, and filtered. Next, we train NovaCOMET on NovATOMIC by fine-tuning an open-source pretrained model. NovaCOMET uses an open-format training objective, replacing the fixed relation sets of past knowledge models, enabling arbitrary structures within the data to serve as inputs or outputs. The resulting generation model, optionally augmented with human annotation, matches or exceeds comparable open task models like Flan-T5 on a range of commonsense generation tasks. NovaCOMET serves as a counterexample to the contemporary focus on instruction tuning only, demonstrating a distinct advantage to explicitly modeling commonsense knowledge as well.

翻译：我们提出NovaCOMET——一种开放常识知识模型，融合了知识模型与通用任务模型的最佳特性。相较于以往的知识模型，NovaCOMET支持开放格式的关系映射，可直接应用于推理任务；相较于Flan-T5等通用任务模型，它明确以知识为核心，在常识推理中展现更优性能。NovaCOMET利用不透明私有模型的知识构建开放知识流水线：首先通过符号化知识蒸馏生成可审计、可评析与可筛选的公开离散知识图谱NovATOMIC；继而基于开源预训练模型在NovATOMIC上进行微调训练。该模型采用开放格式训练目标，摒弃传统知识模型的固定关系集合，允许数据中任意结构作为输入或输出。最终生成的模型（可选配人工标注增强）在多项常识生成任务中达到或超越Flan-T5等同类开放任务模型。NovaCOMET为当前仅关注指令微调的研究范式提供了反例，论证了显式建模常识知识的独特优势。