LMs as Task-Specific Knowledge Bases: An Interpretability Analysis
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 others during training. Parameter localization experiments suggest a mechanistic explanation, revealing distinct parameter subsets underlying di...
| Authors | Amit Elhelo, Amir Globerson, Mor Geva |
|---|---|
| Primary category | cs.CL |
| Published | 25 June 2026 |
| Last updated at source | 6 August 2026 |
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