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In artificial intelligence (AI), commonsense reasoning is a human-like ability to make presumptions about the type and essence of ordinary situations humans encounter every day. These assumptions include judgments about the nature of physical objects, taxonomic properties, and peoples' intentions.
Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. [2]
The Open Mind Common Sense project differs from Cyc because it has focused on representing the common sense knowledge it collected as English sentences, rather than using a formal logical structure. ConceptNet is described by one of its creators, Hugo Liu, as being structured more like WordNet than Cyc, due to its "emphasis on informal ...
Cyc (pronounced / ˈ s aɪ k / SYKE) is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works.
In 2019 a score of 90.1%, was achieved on the original Winograd schema dataset by fine-tuning of the BERT language model with appropriate WSC-like training data to avoid having to learn commonsense reasoning. [10] The general language model GPT-3 achieved a score of 88.3% without specific fine-tuning in 2020. [19]
The training dataset consisting of a set of polysemous nouns/verbs and the sentence instances that they occurred in. ... Common Sense Reasoning, Lexical ...
Examples of such datasets include QNLI (Wikipedia articles) and MultiNLI (transcribed speech, popular fiction, and government reports, among other sources); [7] It similarly outperformed previous models on two tasks related to question answering and commonsense reasoning—by 5.7% on RACE, [8] a dataset of written question-answer pairs from ...
Large dataset that covers a wider range of reasoning abilities Each task consists of input/output, and a task definition. Additionally, each ask contains a task definition. Further information is provided in the GitHub repository of the project and the Hugging Face data card. Input/Output and task definition 2022 [341] Wang et al. LAMBADA