arXiv:2511.20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed. Efficient and accurate knowledge updates without costly retraining are a major challenge. This problem is particularly challenging in lifelong settings, where complex, unstructured knowledge must coexist without interference. We introduce RILKE (Representation Intervention for Lifelong KnowledgE Control), a robust and scalable method that treats knowledge control as interventions within the model's representation space. Leveraging representation-spa
The proliferation of LLMs creates an urgent need for efficient knowledge management, which traditional retraining methods cannot meet, especially in dynamic information environments.
This development addresses a core limitation of LLMs—their inability to dynamically update knowledge without costly retraining—potentially making them more reliable and adaptable for real-world applications.
LLMs can now theoretically manage and update knowledge continuously and precisely, overcoming the 'knowledge cutoff' problem and reducing the operational overhead of maintaining accurate models.
- · AI developers and researchers
- · Enterprises deploying LLMs at scale
- · Cloud providers offering AI services
- · Companies reliant on frequent, full LLM retraining cycles
- · Outdated knowledge management software
LLMs become significantly more practical for applications requiring real-time, accurate information.
The cost of maintaining and operating advanced AI models decreases, accelerating their deployment across industries.
Enhanced LLM reliability could erode trust in human-curated information, particularly in fast-evolving fields.
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Read at arXiv cs.AI