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arxiv: 2606.27237 · v1 · pith:UFPER4J5new · submitted 2026-06-25 · 💻 cs.CL

LMs as Task-Specific Knowledge Bases: An Interpretability Analysis

Amit Elhelo , Amir Globerson , Mor Geva This is my paper

Pith reviewed 2026-06-26 04:12 UTC · model grok-4.3

classification 💻 cs.CL
keywords language modelsfactual knowledgetask-specific encodingparameter localizationinterpretabilitychain-of-thoughtknowledge bases
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The pith

Language models encode the same fact using different parameters depending on the task.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether language models maintain a consistent knowledge base where the same fact yields the same result across different queries. Behavioral experiments show that facts acquired during training on one task often do not appear when the model is tested on other tasks. Parameter localization identifies distinct subsets of weights that support the same fact under different task conditions. Chain-of-thought prompting succeeds in part by recruiting parameters tied to the reasoning task rather than only the final evaluation task. These patterns indicate that knowledge storage and retrieval in models are shaped by the specific task used to access them.

Core claim

Language models 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 reveal distinct parameter subsets underlying different tasks for the same fact. Chain-of-thought reasoning draws part of its effectiveness from engaging task-specific parameters beyond those tied to the evaluation task. The findings indicate that what the model knows and how it is asked are intertwined in parameter space, undermining the knowledge base analogy.

What carries the argument

Task-specific parameter subsets that store the same fact under different query conditions.

If this is right

  • Facts learned on one task do not reliably transfer to other tasks during training.
  • Different tasks for the same fact rely on non-overlapping parameter groups.
  • Chain-of-thought benefits arise from recruiting additional task-specific parameters.
  • Factual knowledge in models cannot be treated as a single unified source.
  • Reliability and controllability of facts depend on the task used to query them.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Knowledge editing methods may need to modify multiple disjoint parameter sets to update a fact consistently across tasks.
  • Training objectives that explicitly encourage parameter sharing could reduce task-specific fragmentation.
  • The pattern raises the possibility that larger models will continue to encode knowledge in task-dependent ways unless training explicitly counters it.
  • Task-specific parameter localization could enable selective control over which facts are accessible under which conditions.

Load-bearing premise

The selected tasks and training dynamics are representative enough that failure of facts to co-emerge reflects task-specific parameter encoding rather than differences in task difficulty or optimization paths.

What would settle it

Finding that the same fact consistently activates overlapping parameters across a broad set of tasks and models during localization experiments would contradict the task-specific encoding claim.

Figures

Figures reproduced from arXiv: 2606.27237 by Amir Globerson, Amit Elhelo, Mor Geva.

Figure 1
Figure 1. Figure 1: Language models and task-invariance. A tra [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples of consistent (top) and inconsistent [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The criteria used to localize and evaluate [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Necessity results for (country, official language, language) on OLMo-2-7B IT. For each fact, we localize a subset of attention heads and MLP neurons for the target task (row). Columns show the effect of ablating that subset on each evaluation task. Values are averaged over facts. Cell color reflects the relative change from baseline (baseline row pinned to green for reference). Large diagonal drops confirm… view at source ↗
Figure 5
Figure 5. Figure 5: CoT versus direct answering under zero￾ablation on (landmark, in-country) for Gemma-2- 9B IT, reported as accuracy. (a) Ablating each (fact, task) pair’s own encoding. (b) For each pair, ablating the other task’s encoding causing the largest drop. Roberts et al., 2020), motivating their view as knowledge bases. Several works have revealed that factual recall is sensitive to query form; para￾phrased prompts… view at source ↗
Figure 6
Figure 6. Figure 6: Example prompts for each task, shown for the [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of fact emergence steps per task. Red dashed lines mark task emergence ( [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Directional co-emergence rates on OLMo-3-7B IT. Each cell reports the co-emergence rate, with pair [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Directional co-emergence rates on OLMo-3-7B IT under a looser ( [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Necessity results on OLMo-2-7B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Necessity results on OLMo-2-13B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Necessity results on Gemma-2-9B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Sufficiency results on Gemma-2-9B IT. Each row shows the reconstruction rate after patching the [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Sufficiency results on OLMo-2-13B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Sufficiency results on OLMo-2-7B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Pairwise entanglement scores Ent(tA→tB) on OLMo-2-7B IT. Rows correspond to the ablated task; columns to the evaluated task. Row and column annotations show the mean score (µ). Discrimination￾tasks exhibit higher entanglement with all other tasks than generation-tasks. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: CoT vs. direct answering under zero-ablation, OLMo-2-7B IT. [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: CoT vs. direct answering under zero-ablation, OLMo-2-13B IT. [PITH_FULL_IMAGE:figures/full_fig_p029_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: CoT vs. direct answering under zero-ablation, Gemma-2-9B IT. [PITH_FULL_IMAGE:figures/full_fig_p030_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: CoT-ablation heatmaps, OLMo-2-7B IT. Rows: ablated task; columns: evaluation task scored under [PITH_FULL_IMAGE:figures/full_fig_p031_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: CoT-ablation heatmaps, OLMo-2-13B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p032_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: CoT-ablation heatmaps, Gemma-2-9B IT. Same layout as Figure [PITH_FULL_IMAGE:figures/full_fig_p033_22.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that language models encode factual knowledge in a task-specific manner rather than as a unified, task-agnostic knowledge base. Behavioral experiments show that facts acquired during training on one task frequently fail to co-emerge when the model is evaluated on other tasks. Parameter localization identifies distinct subsets of parameters supporting the same fact under different tasks. An additional analysis indicates that chain-of-thought reasoning benefits from engaging parameters beyond those tied to the direct evaluation task. These observations are taken to undermine the knowledge-base analogy and to have implications for reliability and controllability of factual outputs.

Significance. If the central claims survive controls for task difficulty and optimization confounds, the work would meaningfully advance LM interpretability by supplying both behavioral and mechanistic evidence that factual recall is entangled with task formulation in parameter space. The training-dynamics approach and the extension to chain-of-thought are constructive contributions that could inform knowledge-editing methods and prompt design. The paper does not supply machine-checked proofs or parameter-free derivations, but the empirical framing is falsifiable in principle.

major comments (2)
  1. [§3] §3 (behavioral analysis): the claim that non-co-emergence of the same fact across tasks indicates task-specific parameter encoding rests on the assumption that the chosen tasks are comparable in difficulty and optimization trajectory. No matched accuracy curves, synthetic controls, or difficulty metrics are reported that would rule out the alternative that differences in learning speed or loss landscapes, rather than distinct fact-specific parameters, drive the observed divergence.
  2. [§4] §4 (parameter localization): the localization procedure identifies subsets whose ablation affects task performance, yet it is not demonstrated that these subsets are independent of task-specific optimization paths or that ablating them selectively impairs the underlying fact across all tasks. Without such a dissociation, the mechanistic interpretation that distinct parameter subsets underlie the same fact remains under-supported.
minor comments (2)
  1. [Figures 1-3] Figure captions and legends should explicitly define the quantitative criterion used for 'co-emergence' (e.g., accuracy threshold and time window) so that the behavioral plots can be interpreted without reference to the main text.
  2. [§4] Notation for the localization metric (e.g., the precise definition of the importance score) would benefit from an explicit equation or pseudocode block.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important considerations for strengthening the behavioral and mechanistic claims. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [§3] §3 (behavioral analysis): the claim that non-co-emergence of the same fact across tasks indicates task-specific parameter encoding rests on the assumption that the chosen tasks are comparable in difficulty and optimization trajectory. No matched accuracy curves, synthetic controls, or difficulty metrics are reported that would rule out the alternative that differences in learning speed or loss landscapes, rather than distinct fact-specific parameters, drive the observed divergence.

    Authors: We selected tasks from established benchmarks with comparable final accuracies and similar input/output formats to mitigate difficulty confounds, and the non-co-emergence pattern holds across multiple random seeds. However, we acknowledge the absence of explicit matched accuracy curves, synthetic controls, or quantitative difficulty metrics. In the revision we will add these: per-task accuracy trajectories plotted against training steps, a synthetic dataset variant controlling for loss landscape properties, and a difficulty metric based on token-level perplexity. These additions will directly test whether divergence arises from task-specific parameter encoding rather than optimization differences. revision: yes

  2. Referee: [§4] §4 (parameter localization): the localization procedure identifies subsets whose ablation affects task performance, yet it is not demonstrated that these subsets are independent of task-specific optimization paths or that ablating them selectively impairs the underlying fact across all tasks. Without such a dissociation, the mechanistic interpretation that distinct parameter subsets underlie the same fact remains under-supported.

    Authors: The localization relies on task-conditioned attribution followed by ablation that impairs fact recall only under the original task formulation. We agree that full dissociation from optimization trajectories and cross-task selectivity has not been shown. In revision we will add (i) localization repeated under alternative optimizers and learning-rate schedules to check trajectory dependence, and (ii) cross-task ablation experiments measuring whether parameters localized for task A impair the same fact when evaluated on task B. These controls will be reported with quantitative effect sizes. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on direct experimental observations without derivations or fitted predictions.

full rationale

The paper presents behavioral results from training dynamics (facts failing to co-emerge across tasks) and parameter localization experiments as evidence for task-specific encoding. These are empirical measurements, not mathematical derivations, predictions from fitted parameters, or self-citation chains. No equations, ansatzes, or uniqueness theorems are invoked that reduce to inputs by construction. The central interpretation follows from the observed data rather than being forced by prior self-referential steps. This is self-contained experimental work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5696 in / 949 out tokens · 36718 ms · 2026-06-26T04:12:43.268806+00:00 · methodology

discussion (0)

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