I've been deeply involved in the AI sector for quite some time now as a software engineer, founder, and previously with roles at Bridgewater Associates and NVIDIA. Recently, I've been pondering the limitations and potential improvements of Large Language Models (LLMs) such as GPT-4.
While LLMs have demonstrated impressive capabilities in understanding and generating human-like text, they are inherently limited by their lack of access to real-time data. This restriction effectively freezes their knowledge at the point of their last training update, leaving them unable to provide information about events or knowledge that have emerged post-training.
Moreover, a significant portion of their training and computational resources is spent on memorizing facts, functioning in essence as massive, intricately structured fact databases. This focus on fact storage often limits their potential for reasoning and creativity.
Given these constraints, I've proposed an approach that aims to shift the focus of LLMs from fact storage to enhanced reasoning. By allowing LLMs to access and integrate real-time data from their training corpus during inference, I believe we can transform these models into dynamic reasoning engines that can provide more accurate, current, and contextually relevant responses, while making more efficient use of their training and computational resources.
I am sharing this here not just to present my idea, but to spark a broader discussion on how we can push the boundaries of what's possible with LLMs. What are your thoughts on this approach? Do you see other potential solutions to the limitations of LLMs? How can we address the challenges that such a transformation might pose?
I am eager to hear your insights and engage in a productive discussion on this topic. Thank you for your time and consideration.
I've been deeply involved in the AI sector for quite some time now as a software engineer, founder, and previously with roles at Bridgewater Associates and NVIDIA. Recently, I've been pondering the limitations and potential improvements of Large Language Models (LLMs) such as GPT-4.
While LLMs have demonstrated impressive capabilities in understanding and generating human-like text, they are inherently limited by their lack of access to real-time data. This restriction effectively freezes their knowledge at the point of their last training update, leaving them unable to provide information about events or knowledge that have emerged post-training.
Moreover, a significant portion of their training and computational resources is spent on memorizing facts, functioning in essence as massive, intricately structured fact databases. This focus on fact storage often limits their potential for reasoning and creativity.
Given these constraints, I've proposed an approach that aims to shift the focus of LLMs from fact storage to enhanced reasoning. By allowing LLMs to access and integrate real-time data from their training corpus during inference, I believe we can transform these models into dynamic reasoning engines that can provide more accurate, current, and contextually relevant responses, while making more efficient use of their training and computational resources.
I've detailed this proposal, along with its technical implementation and associated challenges, in a recent blog post: https://www.workbyjacob.com/thoughts/from-llm-to-rqm-real-ti...
I am sharing this here not just to present my idea, but to spark a broader discussion on how we can push the boundaries of what's possible with LLMs. What are your thoughts on this approach? Do you see other potential solutions to the limitations of LLMs? How can we address the challenges that such a transformation might pose?
I am eager to hear your insights and engage in a productive discussion on this topic. Thank you for your time and consideration.