Summary of Prompting a Pretrained Transformer Can Be a Universal Approximator, by Aleksandar Petrov et al.
Prompting a Pretrained Transformer Can Be a Universal Approximator
by Aleksandar Petrov, Philip H.S. Torr, Adel Bibi
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Functional Analysis (math.FA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the theoretical foundations of prompt tuning and prefix-tuning methods for transformer models. Specifically, it explores whether these fine-tuning techniques can universally approximate sequence-to-sequence functions. The authors demonstrate that smaller pretrained models than previously thought can be universal approximators when prefixed, highlighting the attention mechanism’s unique suitability for this purpose. Furthermore, they show that any sequence-to-sequence function can be approximated by prefixing a transformer with depth linear in the sequence length. The study also provides Jackson-type bounds on the length of the prefix required to approximate a function to a desired precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about understanding how we can modify and improve pre-trained language models like transformers. Researchers have been fine-tuning these models using special prompts or prefixes, but they didn’t know if it was possible to make any model behave in any way just by adjusting the prompt. This study shows that with some clever prefixing, even smaller models can be made to do almost anything, and provides rules for how long the prefix needs to be. |
Keywords
* Artificial intelligence * Attention * Fine tuning * Precision * Prompt * Transformer
