Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning
This addresses the problem of non-linear human thought processes in multi-modal AI for researchers, offering a novel method but likely incremental as it builds on existing chain-of-thought techniques.
The paper tackles the limitation of linear chain-of-thought reasoning in multi-modal tasks by proposing an Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning, achieving good results in tasks like text-image retrieval, visual question answering, and image recognition with improved domain generalization.
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.