Summary of Fundamental Limits Of Prompt Tuning Transformers: Universality, Capacity and Efficiency, by Jerry Yao-chieh Hu et al.
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
by Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani, Chenyang Li, Zhao Song, Han Liu
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We investigate the limitations of prompt tuning for transformer-based foundation models. Our key contributions include prompt tuning on single-head transformers with a single self-attention layer, which is universal and supports almost-linear time algorithms under the Strong Exponential Time Hypothesis (SETH). Statistically, we prove that these transformers are universal approximators for sequence-to-sequence Lipschitz functions. Additionally, we provide an exponential lower bound on the required soft-prompt tokens for prompt tuning to memorize any dataset with 1-layer, 1-head transformers. Computationally, we identify a phase transition in the efficiency of prompt tuning, determined by the norm of soft-prompt-induced keys and queries, and provide an upper bound criterion. Our theory provides important necessary conditions for designing expressive and efficient prompt tuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We studied how to improve the performance of language models using “prompt tuning”. We found that a simple type of model called a single-head transformer can learn any task, as long as it’s given the right instructions. This is important because these models are widely used in many areas, such as natural language processing and machine learning. We also discovered that there’s an optimal way to design prompts for these models, which affects how quickly they can process information. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Prompt » Self attention » Transformer
