6 papers · 1 filter
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…
Evaluating the Effect of Retrieval Augmentation on Social Biases
Tianhui Zhang, Yi Zhou, Danushka Bollegala
Retrieval Augmented Generation (RAG) has gained popularity as a method for conveniently incorporating novel facts that were not seen during the pre-training stage in Large Language…
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +45
People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications wit…
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…