4 papers
Synthetic Data Generation for Training Diversified Commonsense Reasoning Models
Tianhui Zhang, Bei Peng, Danushka Bollegala
Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering multiple plausible alternative sc…
Evaluating the Evaluation of Diversity in Commonsense Generation
Tianhui Zhang, Bei Peng, Danushka Bollegala
In commonsense generation, given a set of input concepts, a model must generate a response that is not only commonsense bearing, but also capturing multiple diverse viewpoints. Num…
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
Tianhui Zhang, Bei Peng, Danushka Bollegala
Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the ge…
Learning to Predict Concept Ordering for Common Sense Generation
Tianhui Zhang, Danushka Bollegala, Bei Peng
Prior work has shown that the ordering in which concepts are shown to a commonsense generator plays an important role, affecting the quality of the generated sentence. However, it…