arXiv:2411.07175v3 Announce Type: replace Abstract: As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is fine-tuning them directly on new knowledge. However, recent studies have shown that fine-tuning for memorization may be ineffective in storing knowledge or may exacerbate hallucinations. In this work, we introduce a setting we call continual memorization, where a model must memorize and retain a set of factoids through multiple stages of fine-tuning on subsequent datasets. We characterized the for
The rapid development and deployment of LLMs highlight the persistent challenge of integrating new, time-sensitive factual knowledge without extensive retraining or performance degradation.
This research addresses a core limitation in maintaining the relevance and accuracy of large language models, impacting their utility in dynamic real-world applications.
New methodologies for continual memorization could allow LLMs to update their knowledge more efficiently and reliably, rather than becoming quickly obsolete.
- · AI developers
- · Enterprises using LLMs
- · Knowledge management platforms
- · LLMs requiring frequent and costly retraining
- · Users relying on outdated or hallucinating models
Language models become more adaptable and reliable for factual recall, reducing the need for costly full-model retraining.
Improved factual accuracy and recency in LLMs could accelerate their deployment in critical, real-time information processing roles.
The ability to continually update knowledge might enable more dynamic and personalized AI agents, further collapsing workflows and improving decision support.
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