  Large Language Models (LLMs) have demonstrated remarkable reasoning
capabilities, notably in connecting ideas and adhering to logical rules to
solve problems. These models have evolved to accommodate various data
modalities, including sound and images, known as multimodal LLMs (MLLMs), which
are capable of describing images or sound recordings. Previous work has
demonstrated that when the LLM component in MLLMs is frozen, the audio or
visual encoder serves to caption the sound or image input facilitating
text-based reasoning with the LLM component. We are interested in using the
LLM's reasoning capabilities in order to facilitate classification. In this
paper, we demonstrate through a captioning/classification experiment that an
audio MLLM cannot fully leverage its LLM's text-based reasoning when generating
audio captions. We also consider how this may be due to MLLMs separately
representing auditory and textual information such that it severs the reasoning
pathway from the LLM to the audio encoder.
