Low-Rank Fine-Grained Prompt Transfer for More Modalities
Authors: Han Jiang, Tao Jin, Zhou Zhao


Reviewer paper summaries: This paper proposed the low-rank fine-grained Prompt (LFP) method to enhance modality interaction between prompts. Specifically, this work proposed the fine-grained prompt (FP) adaptable to more than two modalities, utilizing the EUMP module to fuse prompts of different modalities and the modality-completeness gate considering modality-incomplete scenarios. To further reduce the space complexity, this work utilizes low-rank decomposition to enable low-rank fine-grained prompts (LFP). Experiments are conducted on multimodal sentiment analysis tasks and multimodal video retrieval tasks to compare the effectiveness of the modules.
Summary of strengths: 1. It proposes a novel method to consider the multi-modality case while incorporating the modality incomplete scenarios via prompt tuning. 
2. It includes a clear organization of the related literature on multimodal learning and multimodal prompts learning. Also, the authors include clear formulas to elaborate the proposed modules.
Summary of weaknesses: 1. The implementations in Section 4 need more clarifications:
- what encoders are used for multimodal sentiment analysis tasks
- could include more comparisons between the baseline models and the proposed modules of LFP

2. According to the current experiment results, the impact of the method is limited:
- current experiments look like ablation studies towards the modules of the methods
- needs more comparison with the other methods (w/wo LFP) to demonstrate the effectiveness of this method
Comments, suggestions and typos: 1. Could extend the analysis of experiments in Section 4.3, along with more experiments to verify the effectiveness of this method
2. There could be some further illustrations and analysis of some confusing results: 
- Why would LFP and FP perform differently under different tasks (LFP generally outperforms FP on multimodal video retrieval tasks while LFP and FP outperform each other alternately on different datasets for the multimodal sentiment analysis tasks)?
- How is the lower complexity benefiting LFP under multimodal video retrieval tasks?
Overall assessment scores: 2.5