Summary of Ask, and It Shall Be Given: on the Turing Completeness Of Prompting, by Ruizhong Qiu et al.
Ask, and it shall be given: On the Turing completeness of prompting
by Ruizhong Qiu, Zhe Xu, Wenxuan Bao, Hanghang Tong
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC)
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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 The abstract discusses the large language model (LLM) prompting paradigm, where a general-purpose LLM is trained and then prompted with different inputs to perform various tasks. The authors present the first theoretical study on this phenomenon, showing that prompting is Turing-complete. This means that there exists a finite-size Transformer that can compute any computable function given a corresponding prompt. Additionally, the authors demonstrate that a single finite-size Transformer can achieve nearly the same complexity bounds as an unbounded-size Transformer. Overall, this work provides a theoretical underpinning for prompt engineering and reveals the potential of prompting to enable efficient universality in a single finite-size Transformer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart computers that can understand and generate human-like text. These LLMs are being used to perform many tasks, like answering questions or generating creative writing. But until now, we didn’t really know how they work when given different inputs, or “prompts”. A team of researchers has finally figured out the underlying math behind this process, showing that it’s possible to use a single LLM to do almost anything that another LLM can do. This is important because it could lead to new ways of using these powerful computers in things like language translation, text summarization, and more. |
Keywords
» Artificial intelligence » Large language model » Prompt » Prompting » Summarization » Transformer » Translation
