MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning

• URL: http://arxiv.org/abs/2212.10773v3
• Date: Sat, 10 Jun 2023 18:33:21 GMT
• Title: MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning
• Authors: Zhiyang Xu, Ying Shen, Lifu Huang
• Abstract summary: Instruction tuning is a new learning paradigm that fine-tunes pre-trained language models on tasks specified through instructions. We introduce MUL-TIINSTRUCT, the first multimodal instruction tuning benchmark dataset. We show strong zero-shot performance on various unseen multimodal tasks and the benefit of transfer learning from a text-only instruction dataset.
• Score: 24.741736629886564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on tasks specified through instructions, has shown promising zero-shot performance on various natural language processing tasks. However, it has yet to be explored for vision and multimodal tasks. In this work, we introduce MUL-TIINSTRUCT, the first multimodal instruction tuning benchmark dataset that consists of 62 diverse multimodal tasks in a unified seq-to-seq format covering 10 broad categories. The tasks are derived from 21 existing open-source datasets and each task is equipped with 5 expert-written instructions. We take OFA as the base pre-trained model for multimodal instruction tuning, and to further improve its zero-shot performance, we explore multiple transfer learning strategies to leverage the large-scale NATURAL INSTRUCTIONS dataset. Experimental results demonstrate strong zero-shot performance on various unseen multimodal tasks and the benefit of transfer learning from a text-only instruction dataset. We also design a new evaluation metric - Sensitivity, to evaluate how sensitive the model is to the variety of instructions. Our results indicate that fine-tuning the model on a diverse set of tasks and instructions leads to a reduced sensitivity to variations in instructions for each task.

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