DeepMind Paper Challenges Yann LeCun’s Dismissal of AutoregressiveLLMs, Proving Their Computational Universality
By [Your Name], SeniorJournalist and Editor
The reign of autoregressive large language models (LLMs) continues, despite pronouncements from prominent figures like Yann LeCun thatthey are destined to fade. LeCun, a renowned AI researcher, has been vocal in his criticism of autoregressive models, even predicting their demise within the next fiveyears. However, a recent paper by DeepMind and the University of Alberta presents compelling evidence that challenges LeCun’s assertion, demonstrating that autoregressive LLMs can achieve computational universality.
The paper, titled AutoregressiveLarge Language Models are Computationally Universal, explores the question of whether LLMs can support universal computation when using unbounded thought chains. The researchers found that, without external intervention or model weight modifications, the autoregressive decoding of Transformer-based language models canindeed achieve this capability.
This finding is significant because it directly contradicts LeCun’s argument that autoregressive models are fundamentally limited in their ability to perform complex computations. LeCun advocates for alternative approaches, such as diffusion models, which he believes hold more promise for achieving true AI.
The DeepMind paper,available on arXiv, provides strong evidence to support the claim that autoregressive LLMs can serve as a foundation for general-purpose computation. This opens up exciting possibilities for the development of more sophisticated AI systems capable of tackling complex tasks.
While LeCun’s skepticism towards autoregressive models may stem from theirlimitations in certain areas, such as reasoning and commonsense understanding, the DeepMind paper suggests that these limitations may be addressable through further research and development. The paper’s findings highlight the ongoing evolution of LLMs and the potential for continued innovation in this field.
This research is a crucial step in understanding the capabilities of autoregressive LLMs and their potential to drive advancements in AI. It challenges established narratives and opens up new avenues for exploration, prompting further investigation into the theoretical and practical implications of this groundbreaking discovery.
References:
- Autoregressive Large Language Models are Computationally Universal: https://arxiv.org/pdf/2410.03170
- GPT-4 的研究路径没有前途？Yann LeCun 给自回归判了死刑: https://www.jiqizhixin.com/articles/2023/04/27/14136
Views: 0