Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning
arXiv:2607.08393v1 Announce Type: cross Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textit{\textbf{Knowing--Using Gap}}, characterized by an accuracy gap and a temporal lag between memorization and generalization. To understand this phenomenon, we fine-tune LLMs with unseen knowledge and monitor the spatial permeation dynamics of the knowledge internally using a novel intervention technique called self-patching. Self-patching identifies
This research addresses a fundamental limitation in large language model (LLM) fine-tuning that is becoming increasingly critical as models are deployed for knowledge-intensive tasks.
Understanding the 'Knowing-Using Gap' is crucial for developing more robust and reliable AI systems, especially for applications requiring reasoning beyond simple memorization.
The formalization and mechanistic understanding of why LLMs struggle to generalize memorized facts will guide future research and development towards more effective finetuning strategies.
- · AI researchers and developers
- · Companies building sophisticated AI applications
- · Sectors reliant on AI for complex reasoning
- · Current naive LLM fine-tuning methodologies
- · Applications that assume LLMs inherently generalize new knowledge
Improved fine-tuning techniques will lead to more capable and reliable LLMs for specific domains.
Enhanced LLM reasoning capabilities could accelerate automation in new areas, potentially impacting white-collar workflows.
A deeper understanding of LLM generalization could inform the development of truly agentic AI systems that learn and adapt more effectively.
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