arXiv:2606.27237v1 Announce Type: new Abstract: Language models (LMs) capture large amounts of factual knowledge applicable to a wide range of tasks, motivating the view of their parameters as a knowledge base. An important property of knowledge bases is that different queries for the same fact return consistent results, drawing on a single source of truth. We investigate whether LMs satisfy this property through behavioral and mechanistic analyses. Our results suggest that they encode knowledge in a task-specific manner. Behaviorally, facts acquired on one task frequently fail to co-emerge on
This research builds on contemporary efforts to understand the internal mechanisms of large language models, a rapidly evolving field.
A strategic reader should care because how LMs encode and retrieve information directly impacts their reliability, trustworthiness, and applicability in critical tasks, influencing downstream AI development and deployment strategies.
This paper redefines our understanding of LM knowledge representation, suggesting that knowledge is not globally consistent but task-specific, which poses challenges for efforts to treat LMs as unified knowledge bases.
- · AI interpretability researchers
- · Developers of specialized LMs
- · Users prioritizing task-specific AI reliability
- · Developers relying on LMs as general-purpose, unified knowledge bases
- · Applications requiring high factual consistency across diverse tasks from a sing
Immediate implications for how LMs are fine-tuned and evaluated for factual consistency across different uses.
Heightened focus on developing methods to ensure fact consistency within LMs or employing multi-LM architectures for diverse knowledge demands.
Potential for new AI architectures that explicitly separate core knowledge representation from task-specific adaptations, leading to more robust and scrutable AI systems.
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