研究人员正在探索大型语言模型（LLM）是否能够超越语言任务，执行与传统计算系统类似的计算。研究重点已转向理解 LLM 是否可以使用其内部机制在计算上等同于通用图灵机。传统上，LLM 主要用于自然语言处理任务，如文本生成、翻译和分类。然而，这些模型的计算边界仍有待充分理解。本研究探讨了 LLM 是否可以像图灵机这样的经典模型一样充当通用计算机，而无需外部修改或内存增强。

🤔 研究人员探讨了大型语言模型（LLM）是否可以像图灵机这样的经典模型一样充当通用计算机，而无需外部修改或内存增强。

💡 研究人员通过扩展自回归解码来适应长输入字符串，设计了一种名为 Lag 系统的内部规则系统，该系统模拟了类似于经典图灵机中的内存操作。

🤖 研究人员通过创建一个名为 gemini-1.5-pro-001 的 LLM 系统提示，使其在确定性（贪婪）解码下应用 2,027 个生产规则，这些规则模拟了 Lag 系统。

🚀 研究人员通过配置语言模型来模拟特定通用图灵机 U15,2 来评估所提出方法的性能，该通用图灵机由 2,027 个生产规则和 262 个符号的字母表定义。

🎉 研究证实，在所提出的框架下，gemini-1.5-pro-001 可以正确应用这些规则，以在通用图灵机的理论框架内执行任何计算。

🌟 研究结果表明，LLM 在特定条件下可以模拟传统计算机可以实现的任何计算任务。

✨ 研究证实，当与定义明确的生产规则系统相结合时，广义自回归解码可以将语言模型转换为通用计算实体。

💫 研究人员证明了在模型上下文窗口的限制内实现复杂计算任务的可行性，方法是在解码过程中动态管理内存状态。

💯 研究证明可以使用单个系统提示来实现复杂计算，这为高级计算任务的 LLM 设计和利用提供了新的视角。

🚀 研究结果表明，LLM 可以自主地执行计算任务，而无需外部修改或内存增强。

🚀 扩展的自回归解码方法允许语言模型处理超出其上下文窗口的序列，证明它可以对无界输入序列执行计算。

🚀 该框架确立了大型语言模型可以通过其内部机制充当通用计算机器的能力。

🚀 研究还表明，gemini-1.5-pro-001 可以使用 2,027 个生产规则和 262 个符号的字母表来模拟通用图灵机。

🚀 研究对 LLM 在计算领域的潜力提供了新的见解，并表明它们可以用于解决传统计算机难以处理的复杂问题。

Researchers are investigating whether large language models (LLMs) can move beyond language tasks and perform computations that mirror traditional computing systems. The focus has shifted towards understanding whether an LLM can be computationally equivalent to a universal Turing machine using only its internal mechanisms. Traditionally, LLMs have been used primarily for natural language processing tasks like text generation, translation, and classification. However, the computational boundaries of these models still need to be fully understood. This study explores whether LLMs can function as universal computers, similar to classical models like Turing machines, without requiring external modifications or memory enhancements.

The primary problem addressed by the researchers is the computational limitations of language models, such as transformer architectures. While these models are known to perform sophisticated pattern recognition and text generation, their ability to support universal computation, meaning they can perform any calculation that a conventional computer can, is still debated. The study seeks to clarify whether a language model can autonomously achieve computational universality using a modified autoregressive decoding mechanism to simulate infinite memory and processing steps. This investigation has significant implications, as it tests the fundamental computational limits of LLMs without relying on external intervention or specialized hardware modifications.

Existing methods that aim to push the computational boundaries of LLMs typically rely on auxiliary tools like external memory systems or controllers that manage and parse outputs. Such approaches extend the models’ functionality but detract from their standalone computational capabilities. For instance, a previous study demonstrated how augmenting LLMs with a regular expression parser could simulate a universal Turing machine. While this showed promise, it did not prove that the LLM was responsible for the computation, as the parser played a significant role in offloading complex tasks. Thus, whether LLMs can independently support universal computation has yet to be solved.

Researchers from Google DeepMind and the University of Alberta introduced a novel method by extending autoregressive decoding to accommodate long input strings. They designed an internal system of rules called a Lag system that simulates memory operations akin to those in classical Turing machines. This system dynamically advances the language model’s context window as new tokens are generated, enabling it to process arbitrarily long sequences. This method effectively transforms the LLM into a computationally universal machine capable of simulating the operations of a universal Turing machine using only its transformations.

The research involved creating a system prompt for an LLM named gemini-1.5-pro-001 that drives it to apply 2,027 production rules under deterministic (greedy) decoding. These rules simulate a Lag system, which has been computationally universal since the 1960s. The researchers built on this classical theory by developing new proof that the Lag system could emulate a universal Turing machine using a language model. This innovative approach reframes the language model’s decoding process into a sequence of discrete computational steps, making it behave as a general-purpose computer.

The proposed method’s performance was evaluated by configuring the language model to simulate a specific universal Turing machine, U15,2, defined by 2,027 production rules over an alphabet of 262 symbols. The study confirmed that gemini-1.5-pro-001, under the proposed framework, could apply these rules correctly to perform any computation within the theoretical framework of a universal Turing machine. This experiment established a clear correspondence between the language model’s operations and classical computational theory, affirming its ability to act as a general-purpose computing machine using only its internal mechanisms.

This study yields several key findings, which are as follows: 

First, it establishes that language models can, under certain conditions, simulate any computational task achievable by a traditional computer. Second, it validates that generalized autoregressive decoding can convert a language model into a universal computing entity when combined with a well-defined production rules system. Third, the researchers demonstrate the feasibility of implementing complex computational tasks within the constraints of the model’s context window by dynamically managing the memory state during the decoding process. Finally, it proves that complex computations can be achieved using a single system prompt, offering new perspectives on the design and utilization of LLMs for advanced computational tasks.

Key takeaways from the research:

The study demonstrated that gemini-1.5-pro-001 could simulate a universal Turing machine using 2,027 production rules and an alphabet of 262 symbols.The model was shown to execute computational tasks autonomously without external modifications or memory enhancements.The extended autoregressive decoding method allowed the language model to process sequences longer than its context window, proving that it can perform computations over unbounded input sequences.The framework established that large language models can achieve computational universality, similar to classical models like Turing machines.The research revealed that a single prompt could drive a model to perform complex computations, transforming the language model into a standalone general-purpose computer.

In conclusion, this research contributes significantly to understanding the intrinsic computational capabilities of large language models. It challenges the conventional views on their limitations by demonstrating that these models can simulate the operations of a universal Turing machine using only their transformations and prompts. It paves the way for exploring new, more complex applications of LLMs in theoretical and practical settings.

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