Study evaluates Gemini, a multimodal large language model, focusing on its commonsense reasoning abilities.
Commonsense reasoning tested includes general, contextual, temporal, physical, numerical, social, and moral domains.
Gemini was compared against LLMs and MLLMs using various datasets and tasks, employing different prompting techniques.
Results show Gemini's competitive performance but with difficulties in temporal, social reasoning, and emotion discernment in images.
The paper highlights the progress and current limitations of AI commonsense reasoning, with insights for future AI improvements.
The study presents an evaluation of a cutting-edge multimodal large language model (MLLM) known as Gemini, particularly focusing on its commonsense reasoning abilities. Commonsense reasoning is a core cognitive skill that humans use daily to make sense of ordinary situations and complex tasks. This proficiency is challenging to replicate in NLP systems. The research's central drive is to test Gemini's performance extensively and highlight the common challenges current LLMs and MLLMs face in commonsense tasks.
Commonsense reasoning involves an array of domains including general, contextual, temporal, physical and numerical understanding, as well as social interactions and moral judgments. These aspects cover intuitive human understanding, predicting scenarios based on cause and effect, recognizing social cues, ethical reasoning, and interpreting visual information. It's crucial for AI systems to navigate these domains effectively to mirror human understanding and interaction.
The empirical study encompasses the evaluation of Gemini against twelve different datasets. Four popular LLMs are assessed for language-based tasks, while two MLLMs are scrutinized for multimodal tasks. These tasks span various domains of commonsense reasoning such as general, specialized, social, ethical, and visual understanding, with a performance metric of accuracy used across all datasets. Different setups like zero-shot standard prompting and few-shot chain-of-thought prompting are employed to understand the inherent and enhanced commonsense capabilities of the models.
Findings reveal that Gemini's performance is akin to GPT-3.5 Turbo, outperforming it marginally in language-based commonsense reasoning tasks, but lagging behind GPT-4 Turbo. While demonstrating a sturdy understanding of most tested domains, Gemini struggles with temporal and social reasoning, as well as discerning emotion in images. Despite its notable logical reasoning, it often misunderstands context. The study acknowledges limitations, such as the language and dataset scope potentially not covering all commonsense facets, and the resulting outcomes being bound to evolving AI capabilities.
This comprehensive assessment indicates significant progress in AI's ability to reason with commonsense knowledge, yet highlights the nuanced and context-dependent nature of human reasoning remains a tough challenge. The area of multimodal reasoning, combining visual cues with language understanding, is still notably challenging. The detailed examination of the performance across diverse datasets provides valuable insights into the strengths and weaknesses of current LLMs and MLLMs, suggesting a path forward for improvements in natural, robust AI comprehension and interaction.
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