Multimodal Multi-Hop Question Answering Through a Conversation Between Tools and Efficiently Finetuned Large Language Models

• URL: http://arxiv.org/abs/2309.08922v1
• Date: Sat, 16 Sep 2023 08:22:22 GMT
• Title: Multimodal Multi-Hop Question Answering Through a Conversation Between Tools and Efficiently Finetuned Large Language Models
• Authors: Hossein Rajabzadeh, Suyuchen Wang, Hyock Ju Kwon, Bang Liu
• Abstract summary: We employ a tool-interacting divide-and-conquer strategy to answer complex multi-hop questions. To increase the reasoning ability of LLMs, we prompt chatGPT to generate a tool-interacting divide-and-conquer dataset. To assess the effectiveness of this approach, we conduct an evaluation on two recently introduced complex question-answering datasets.
• Score: 20.52053559484399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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