Vision-Language Models, such as GPT-4V, have made impressive achievements in various vision language tasks, but how do they fare in visual deductive reasoning? By employing Raven’s Progressive Matrices, researchers have gauged VLMs’ abilities to execute complex relational and deductive reasoning solely from visual data. Through evaluations on datasets such as Mensa IQ tests and RAVEN, it’s been found that VLMs still fall short in comparison to their text-based reasoning counterparts. This gap stems from challenges they face in discerning and processing abstract visual patterns. For deeper insights into these intriguing findings, visit here.

• VLMs like GPT-4V showcase significant potential.
• They face challenges in multi-hop relational and deductive reasoning.
• Evaluations indicate a gap in visual versus text-based reasoning prowess.
• Difficulty in interpreting abstract visual patterns hinders performance.
• Research highlights need for further VLM advancement.

This examination of VLMs’ deductive reasoning capabilities is crucial, not only for the development of smarter and more competent systems but also to inform the direction of future research aimed at integrating visual and linguistic domains for a more holistic understanding of both worlds.