Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language
  Models
Yuqing Wang Yun Zhao
The burgeoning interest in Multimodal Large Language Models (MLLMs), such as
OpenAI's GPT-4V(ision), has significantly impacted both academic and industrial
realms. These models enhance Large Language Models (LLMs) with advanced visual
understanding capabilities, facilitating their application in a variety of
multimodal tasks. Recently, Google introduced Gemini, a cutting-edge MLLM
designed specifically for multimodal integration. Despite its advancements,
preliminary benchmarks indicate that Gemini lags behind GPT models in
commonsense reasoning tasks. However, this assessment, based on a limited
dataset (i.e., HellaSWAG), does not fully capture Gemini's authentic
commonsense reasoning potential. To address this gap, our study undertakes a
thorough evaluation of Gemini's performance in complex reasoning tasks that
necessitate the integration of commonsense knowledge across modalities. We
carry out a comprehensive analysis of 12 commonsense reasoning datasets,
ranging from general to domain-specific tasks. This includes 11 datasets
focused solely on language, as well as one that incorporates multimodal
elements. Our experiments across four LLMs and two MLLMs demonstrate Gemini's
competitive commonsense reasoning capabilities. Additionally, we identify
common challenges faced by current LLMs and MLLMs in addressing commonsense
problems, underscoring the need for further advancements in enhancing the
commonsense reasoning abilities of these models.