"We empirically study how several baseline models perform on the task of explainable visual entailment, investigating both off-the-shelf and finetuned model performances."
"LLaVA is one of the simplest, yet one of the most high-performing VLM architectures currently available, utilizing a pretrained large language model aligned with vision encoders."
This segment of the study analyzes various AI models to evaluate their effectiveness in explainable visual entailment tasks. The researchers employed both off-the-shelf and fine-tuned models, focusing primarily on LLaVA-1.6, a high-performing visual language model that integrates large language models with vision encoders. Different configurations of LLaVA were tested, including zero-shot conditions as well as utilizing Compositional Chain-of-Thought Prompting, highlighting its potential without the need for extensive fine-tuning.
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