Original Paper: https://arxiv.org/abs/2305.18787
By: Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh
Abstract:
Despite the demonstrated empirical efficacy of prompt tuning to adapt a pretrained language model for a new task, the theoretical underpinnings of the difference between "tuning parameters before the input" against "the tuning of model weights" are limited. We thus take one of the first steps to understand the role of soft-prompt tuning for transformer-based architectures. By considering a general purpose architecture, we analyze prompt tuning from the lens of both: universal approximation and limitations with finite-depth fixed-weight pretrained transformers for continuous-valued functions. Our universality result guarantees the existence of a strong transformer with a prompt to approximate any sequence-to-sequence function in the set of Lipschitz functions. The limitations of prompt tuning for limited-depth transformers are first proved by constructing a set of datasets, that cannot be memorized by a prompt of any length for a given single encoder layer. We also provide a lower bound on the required number of tunable prompt parameters and compare the result with the number of parameters required for a low-rank update (based on LoRA) for a single-layer setting. We finally extend our analysis to multi-layer settings by providing sufficient conditions under which the transformer can at best learn datasets from invertible functions only. Our theoretical claims are also corroborated by empirical results.
The field of artificial intelligence (AI) is constantly advancing, with the efficiency of model training methods being a key focus area. Among these methods, prompt tuning is particularly noteworthy for its application in large-scale transformer models.
This technique aims to make the training process more manageable by fine-tuning a small set of parameters, offering a potential solution for AI engineers facing the challenges of computational and financial resources.
However, prompt tuning also has its limitations and obstacles. This blog post aims to simplify the concept of prompt tuning, discussing its potential and its limitations, especially for AI engineers in enterprise settings.
Prompt tuning simplifies the AI process. Instead of adjusting all parameters of a transformer model during training, it modifies just the input with tunable "prompts," leaving the rest of the model unchanged. This method is appealing because it could allow for adapting large models to specific tasks more efficiently.