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Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms by Ye, Xiaotian; Zhang, Mengqi; Wu, Shu is a scholarly article available to read on EtoBox.

What is Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms about?

Knowledge is fundamental to the overall capabilities of Large Language Models (LLMs). The knowledge paradigm of a model, which dictates how it encodes and utilizes knowledge, significantly affects its performance. Despite the continuous development of LLMs under existing knowledge paradigms, issues within these frameworks continue to constrain model potential. This blog post highlight three critical open problems limiting model capabilities: (1) challenges in knowledge updating for LLMs, (2) the failure of reverse knowledge generalization (the reversal curse), and (3) conflicts in internal knowledge. We review recent progress made in addressing these issues and discuss potential general solutions. Based on observations in these areas, we propose a hypothetical paradigm based on Contextual Knowledge Scaling, and further outline implementation pathways that remain feasible within contemporary techniques. Evidence suggests this approach holds potential to address current shortcomings, serving as our vision for future model paradigms. This blog post aims to provide researchers with a brief overview of progress in LLM knowledge systems, while provide inspiration for the development of n

Author

Ye, Xiaotian; Zhang, Mengqi; Wu, Shu

Published

2025

Language

EN