Commonsense Reasoning
Commonsense reasoning, the ability of AI systems to understand and apply everyday knowledge, is a crucial area of research aiming to bridge the gap between human and artificial intelligence. Current research focuses on integrating large language models (LLMs) with other modalities like vision and tactile data, often using techniques like instruction tuning, multimodal learning, and knowledge graph integration to improve performance on various benchmarks. This work is significant because enhanced commonsense reasoning is essential for building more robust, reliable, and explainable AI systems across diverse applications, including robotics, deepfake detection, and conversational AI.
Papers
Editing Common Sense in Transformers
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, Niket Tandon
Getting MoRE out of Mixture of Language Model Reasoning Experts
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, Jordan Boyd-Graber
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations
Wenting Zhao, Justin T. Chiu, Claire Cardie, Alexander M. Rush