  We employ a tool-interacting divide-and-conquer strategy enabling large
language models (LLMs) to answer complex multimodal multi-hop questions. In
particular, we harness the power of large language models to divide a given
multimodal multi-hop question into unimodal single-hop sub-questions to be
answered by the appropriate tool from a predefined set of tools. After all
corresponding tools provide the LLM with their answers, the LLM generates the
next relevant unimodal single-hop question. To increase the reasoning ability
of LLMs, we prompt chatGPT to generate a tool-interacting divide-and-conquer
dataset. This dataset is then used to efficiently finetune the corresponding
LLM. To assess the effectiveness of this approach, we conduct an evaluation on
two recently introduced complex question-answering datasets. The experimental
analysis demonstrate substantial improvements over existing state-of-the-art
solutions, indicating the efficacy and generality of our strategy
