Generative CommonSense Reasoning

Generative commonsense reasoning (GCR) focuses on enabling machines to generate coherent and plausible text based on everyday knowledge, addressing the limitations of current language models in understanding and reasoning about implicit contextual information. Research actively explores methods to improve both the quality and diversity of generated text, often leveraging large language models augmented with knowledge graphs, multimodal retrieval (text and images), and techniques like in-context learning and knowledge distillation to enhance reasoning capabilities. These advancements hold significant potential for improving conversational AI, question answering systems, and other natural language processing applications that require nuanced understanding of real-world situations.