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.

NovaCOMET 提供了一个开放的常识知识模型，结合了知识和一般任务模型的最佳方面。与以前的知识模型相比，NovaCOMET 允许开放格式关系，可直接应用于推理任务；与 Flan-T5 等一般任务模型相比，它明确地聚焦于知识，从而实现了卓越的常识推理性能。