方法证据记录

Commonsense Reasoning

Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events.

Sources recorded, not reviewed

源记录

引文逐字复制自方法源记录。这些引文不代表任何层级的验证。

Commonsense Reasoning in NLP

分类方法记录 · process-pipeline / text-mining

打开完整方法

精选声明

声明已持久化到证据分类账中，每个声明都有自己的评估。

尚无精选声明

当分类账中没有声明时，此视图不会自行创建声明评估。

相关方法

从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。

Same method familyBERT Embeddingsmachine-suggested · Relational suggestion, not evidence.Same method familyKnowledge Graph Constructionmachine-suggested · Relational suggestion, not evidence.Same method familyMachine Reading Comprehensionmachine-suggested · Relational suggestion, not evidence.Same method familyQuestion Answeringmachine-suggested · Relational suggestion, not evidence.Same method familyRetrieval-Augmented Generationmachine-suggested · Relational suggestion, not evidence.Same method familySemantic Role Labelingmachine-suggested · Relational suggestion, not evidence.