Summary Memory Augmented Large Language Models Computationally Universal arxiv.org
7,344 words - PDF document - View PDF document
One Line
The study explores the computational universality of the Flan-U-PaLM 540B language model, which can handle large inputs and simulate any algorithm.
Slides
Slide Presentation (10 slides)
Key Points
- Transformer-based large language models, when augmented with external memory, are computationally universal.
- Flan-U-PaLM 540B language model has the potential to process arbitrarily large inputs and simulate any algorithm.
- Stored instruction computers can simulate universal Turing machines using a "prompt program" that drives the system.
- The update function in memory-augmented language models parses strings and performs arithmetic operations on memory labels.
- The concept of universal Turing machines and the identification of the smallest known universal Turing machine, U 15,2, are discussed.
- The prompt program utilizes a Turing machine with a memory location to track the position of the tape head.
- The behavior of U 15,2 must be simulated by considering the initial contents of the tape memory.
- The computational universality of memory-augmented large language models was studied to explore their capabilities beyond natural language processing.
Summaries
29 word summary
Transformer-based large language models with external memory are computationally universal, enabling them to handle large inputs and simulate any algorithm. The study focuses on the Flan-U-PaLM 540B language model.
37 word summary
Transformer-based large language models, when augmented with external memory, are computationally universal, allowing them to process arbitrarily large inputs and simulate any algorithm. The study explores the Flan-U-PaLM 540B language model and its ability to simulate a
405 word summary
Transformer-based large language models, when augmented with external memory, are computationally universal. This means that these models have the potential to process arbitrarily large inputs and simulate any algorithm. The study shows that the Flan-U-PaLM 540B language
A stored instruction computer can simulate a universal Turing machine by using a specific "prompt program" that drives the system. The simulation's fidelity is determined by checking a finite set of prompt-result behaviors and verifying the language model's output for each possible input prompt
The update function updates the memory by parsing a string and performing arithmetic operations on the memory labels. The language model retrieves input prompts from a special memory location and uses it as an instruction register. Stored memory values can be accessed by splicing them into the
The paper discusses the concept of universal Turing machines and the identification of the smallest known universal Turing machine, U 15,2, which uses 15 states and 2 tape symbols. The transition table for U 15,2 is provided in Table
The prompt program described in the text utilizes a Turing machine with a memory location 'i' to track the position of the tape head. Moving the head left is represented by i-=1 and moving it right is represented by i+=1. The post
The text explains that each instruction string in Table 1 mimics the logic of the corresponding states in the table. It also mentions that the behavior of U 15,2 must be simulated by considering the initial contents of the tape memory. To do
The post-processing phase of the Turing machine writes the symbol '0' to the current memory location and moves the head right by 1. A similar process occurs when the current input is 1. The language model Flan-U-PaLM
The text excerpt discusses a study on the computational universality of memory-augmented large language models. The study aimed to determine if these models could perform computations beyond natural language processing. The results showed that with the use of external memory, large language models
This document references several studies and papers related to the universality and computational capabilities of large language models. It mentions a study by Chung et al. (2022) on scaling instruction-finetuned language models, as well as a paper by Cook (
This summary provides a list of references cited in the document "Memory Augmented Large Language Models Computationally Universal." The references include works by Shannon, Siegelmann and Sontag, Sipser, Turing, von Neumann, Wei et