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arXiv · 2404.04538

Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning

Juncheng Yang·Zuchao Li·Shuai Xie·Wei Yu·Shijun Li·Bo Du
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Abstract

The chain-of-thought technique has been received well in multi-modal tasks. It is a step-by-step linear reasoning process that adjusts the length of the chain to improve the performance of generated prompts. However, human thought processes are predominantly non-linear, as they encompass multiple aspects simultaneously and employ dynamic adjustment and updating mechanisms. Therefore, we propose a novel Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning in multi-modal representation learning. The proposed AGoT models the human thought process not only as a chain but also models each step as a reasoning aggregation graph to cope with the overlooked multiple aspects of thinking in single-step reasoning. This turns the entire reasoning process into prompt aggregation and prompt flow operations. Experiments show that our multi-modal model enhanced with AGoT soft-prompting achieves good results in several tasks such as text-image retrieval, visual question answering, and image recognition. In addition, we demonstrate that it has good domain generalization performance due to better reasoning.

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Juncheng Yang, Zuchao Li, Shuai Xie, Wei Yu, Shijun Li, Bo Du. 2024-04-06. Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning. https://arxiv.org/abs/2404.04538

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