NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge
  Distillation
Peter West Ronan Le Bras Taylor Sorensen Bill Yuchen Lin Liwei Jiang Ximing Lu Khyathi Chandu Jack Hessel Ashutosh Baheti Chandra Bhagavatula Yejin Choi
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.