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

我们采用一种工具交互的分治策略，使大型语言模型能够回答复杂的多模式多跳问题，并以预定义的工具集中的适当工具回答单模式单跳子问题。通过生成工具交互分治数据集并对相应的语言模型进行高效微调，我们增加了大型语言模型的推理能力。实验分析表明，相比现有的最先进解决方案，我们的策略显著改进了两个最近引入的复杂问答数据集上的效果，表明我们策略的功效和普适性。