N ova COMET : Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023)

Abstract: 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-releaseddiscrete 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.

## introduction we present novacomet, an open commonsense knowledge model combining the advantages of both knowledge models and general task models. novacomet models commonsense knowledge with an open format, allowing it to be applied to general reasoning tasks in contrast to previous knowledge models. compared to simply training ## novacomet: open commonsense models novacomet is a large-scale, open commonsense model that can handle both explicit knowledge generation, and tasks that require common-sense reasoning. novacomet is trained with symbolic knowledge distillation @xcite by combining the commonsense data generated by large language models ( §2.1) with high-quality annotations of knowledge plausibility ( §2.2). we experiment with multiple methods for combining generated data with plausibility information (indicating how likely a given knowledge is) to train the final model, novacomet ( §2.3). ## conclusions overall, we introduce novacomet, an open commonsense foundation model. novacomet takes advantage of closed proprietary models, resulting in an open pipeline and resources that are publicly available. novacomet is trained on data generated from these closed proprietary models and augmented with human annotations, resulting in both a high-quality plausibility model and improved generative model. novacomet surpasses other general models of similar size at a range of commonsense knowledge-intensive tasks, demonstrating the existing need for explicit knowledge modeling, even as task-focused methods like instruction tuning grow in popularity.