Commonsense reasoning is an essential and intuitive facet of human cognition that enables us to interact with the world. Artificial intelligence has come a long way in its attempt to replicate this ability in the form of Natural Language Processing (NLP) and Multimodal Large Language Models (MLLMs). However, these models often struggle to mimic the nuanced commonsense reasoning innate to humans, encompassing basic knowledge, social interactions, moral reasoning, and visual interpretation.
Thankfully, researchers from Stanford University and Meta have introduced models like Gemini Pro and Gemini Pro Vision to tackle these challenges. These models have been tailored for multimodal integration and have shown impressive results in commonsense reasoning tasks across multiple domains. They have been rigorously tested across 12 diverse datasets designed to probe different dimensions of commonsense reasoning, and have shown great progress in language-based and multimodal scenarios.
Notably, Gemini Pro Vision has demonstrated proficiency in analyzing graphic scenes and predicting potential consequences which is a crucial aspect of visual commonsense reasoning. However, all models still grapple with understanding complex scenarios and abstract ideas, which encompass a critical area for improvement.
Clearly, there is still a long way to go in perfecting AI systems that are able to accurately simulate human-like commonsense reasoning. Future research can focus on refining models’ capabilities in specialized domains while improving the nuanced recognition of mental states and emotions in multimodal contexts. With further advancements, we can look forward to AI systems that can truly understand complex scenarios and abstract ideas in the same way that humans do.
