A Multimodal Approach to Estimate Large Language Model Improvisational Capabilities

Abstract

Evaluating the improvisational capabilities of large language models (LLMs) like ChatGPT-4, Mistral, and Anthropic Claude across textual, visual, and psychological domains provides critical insights into their functionality and potential applications. The research demonstrates significant variances in the ability of these models to generate creative, contextually appropriate responses, visually coherent images from textual descriptions, and emotionally nuanced interactions. ChatGPT-4 excelled in textual improvisation, showcasing its capacity to produce linguistically rich and innovative content that pushes the boundaries of traditional text-based AI applications. Mistral distinguished itself in the generation of visual content, effectively translating abstract textual prompts into detailed and contextually relevant images, indicating its utility in creative and design fields. Anthropic Claude performed exceptionally well in psychological adaptability, interpreting and responding to emotional cues with a high degree of empathy and accuracy, suitable for customer service and therapeutic applications. The findings underscore the diverse capabilities of these LLMs, highlighting their potential to transform industries that require nuanced understanding and generation of complex content. Future research should focus on enhancing the reliability of these models across varied scenarios, improving their ethical deployment, and exploring hybrid approaches to leverage their unique strengths.