Prompt Tuning for Generative Multimodal Pretrained Models (2208.02532v1)

Abstract: Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining. In this work, we explore the transfer of prompt tuning to multimodal pretraining, with a focus on generative multimodal pretrained models, instead of contrastive ones. Specifically, we implement prompt tuning on the unified sequence-to-sequence pretrained model adaptive to both understanding and generation tasks. Experimental results demonstrate that the light-weight prompt tuning can achieve comparable performance with finetuning and surpass other light-weight tuning methods. Besides, in comparison with finetuned models, the prompt-tuned models demonstrate improved robustness against adversarial attacks. We further figure out that experimental factors, including the prompt length, prompt depth, and reparameteratization, have great impacts on the model performance, and thus we empirically provide a recommendation for the setups of prompt tuning. Despite the observed advantages, we still find some limitations in prompt tuning, and we correspondingly point out the directions for future studies. Codes are available at \url{https://github.com/OFA-Sys/OFA}

An Expert Overview of "Prompt Tuning for Generative Multimodal Pretrained Models"

The paper "Prompt Tuning for Generative Multimodal Pretrained Models" explores the application of prompt tuning within the field of generative multimodal pretrained models, specifically moving beyond its established success in natural language and vision contrastive pretraining. The focus is on determining the effectiveness of prompt tuning compared to conventional finetuning, especially within a sequence-to-sequence framework adaptable to both understanding and generation tasks. The authors present empirical evidence demonstrating that prompt tuning—a technique requiring minimal parameter adjustments—can achieve performance levels comparable to finetuning, while offering enhanced robustness against adversarial attacks.

Key Results and Observations

The paper conducts thorough experiments across multiple multimodal tasks such as referring expression comprehension, visual entailment, image captioning, and visual question answering (VQA). The results indicate that while prompt tuning may lag behind finetuning for base-size models, it achieves near-equivalent performance with large-size models, reinforcing its potential for efficiency and robustness. Notably, prompt tuning consistently outperforms other parameter-efficient methods such as Adapter and BitFit across all evaluated tasks.

The investigation into experimental factors like prompt length, depth, and reparameterization reveals that:

• Longer prompt sequences generally result in better performance, with a recommendation of 64 tokens for average optimized results.
• Prompt embeddings inserted across both encoder and decoder layers yield the best outcomes, suggesting prompt placement's critical role.
• The impact of reparameterization with additional trainable parameters is task-dependent, with no significant performance boost observed universally.

Implications and Future Directions

The research implies substantial practical advantages for deploying models in resource-constrained environments, given the reduced computational burden of prompt tuning compared to finetuning. The enhanced robustness against adversarial attacks further positions prompt tuning as a viable choice for secure applications. These characteristics underscore the technique's suitability for extending the capabilities of generative multimodal pretrained models in real-world settings.

The analysis flags areas for further research, particularly addressing prompt tuning's slow convergence and sensitivity to hyperparameters. Advancing methods to expedite convergence and streamline hyperparameter tuning may bolster prompt tuning's viability over finetuning. Additionally, leveraging the improved robustness in adversarial settings could catalyze developments in secure AI applications.

Conclusion

The paper presents a comprehensive examination of prompt tuning in the context of generative multimodal pretrained models, offering valuable insights into its efficacy and potential as a lighter-weight alternative to finetuning. While challenges such as training stability and computational resource consumption persist, the demonstrated robustness and comparable performance to finetuning highlight prompt tuning's significant promise. Future research should aim to refine these methods, ultimately enhancing their applicability and efficiency in diverse AI applications.