PEMCAMP: Parameter Efficient Model with Continuous Attentive Multimodal Prompt for Few-Shot Multimodal Sarcasm Detection
Authors: Soumyadeep Jana, Animesh Dey, Ranbir Singh Sanasam


Reviewer paper summaries: This paper provides a parameter-efficient tuning method called PEMCAMP for sarcasm detection. It includes an additional attention-based module and multimodal prompt for prompting. Image modality, text modality and the image caption modality are considered together to be the prompt part. Experiments show that compared with other discrete-based prompting, PEMCAMP performs much better and uses only 0.3 fraction of the entire PLM parameter.
Summary of strengths: 1. It includes multiple experiments on the different multimodal sarcasm dataset and provides multiple ablation studies and comparison with discrete token-based methods.
2. Images included in the paper provide clear guidance for their methods.
Summary of weaknesses: 1. Paper writing needs to be polished. For some part of the paper, it reads unofficial like to include words of "keep this in mind." at line #308.
2. Limited to BERT architecture instead of the latest LLM-based multimodal models or other larger multimodal models.
3. Lack of comparison with other parameter-efficient fine tuning method purely on text. If its performance is similar with text-only prompting, the multimodal part is useless.
4. Lack of detailed analysis of the function of the attention layer.
Comments, suggestions and typos: Q1: For the experiments on MMSD,  PEMCAMP without a caption is almost the same with the text-only part. Does it indicate that image has no use at all for the MMSD?
Q2: Does image modality actually help during the prompting-based training process? What does the attention layer learn? If the multimodal attention layer actually learns almost no multimodal information, then what is the main difference between the method with traditional parameter-efficient tuning?
Overall assessment scores: 2.5