ArXiv

LMs as Task-Specific Knowledge Bases: An Interpretability Analysis

2026-06-25 · 16:22 UTC ·Amit Elhelo, Amir Globerson, Mor Geva ·1 min read
Authors
Amit Elhelo, Amir Globerson, Mor Geva
Categories
cs.CL
arXiv
https://arxiv.org/abs/2606.27237v1
PDF
https://arxiv.org/pdf/2606.27237v1

Brief

Language models' factual knowledge is studied via behavioral and mechanistic analyses (Elhelo et al., arXiv 2026-06-25). The authors find facts learned on one task often do not appear on others, and parameter-localization shows different parameter subsets encode the same fact for different tasks. Chain-of-thought partly succeeds by activating task-specific parameters. Summary based on the abstract (full text not accessed).

Why it matters

Elhelo, Globerson, and Geva (ArXiv preprint, 2026-06-25) show language models encode factual knowledge in a task-specific way: behavioral experiments find facts acquired on one task frequently fail to co-emerge on others during training.

Key details

  • Mechanistic parameter-localization experiments reveal distinct parameter subsets support the same fact across tasks, and chain-of-thought prompts partly work by engaging task-specific parameters beyond those tied to the evaluation task—undermining the 'single source' knowledge-base analogy and raising reliability/controllability concerns.
Source evidence

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 others during training. Parameter localization experiments suggest a mechanistic explanation, revealing distinct parameter subsets underlying different tasks for the same fact. Finally, we show that chain-of-thought reasoning draws part of its effectiveness from engaging task-specific parameters beyond those tied to the evaluation task. Our findings suggest that what the model knows and how it is asked are intertwined in parameter space, undermining the "knowledge base" analogy and carrying implications for the reliability and controllability of factual knowledge in LMs.