arxiv: 2604.19934 · v2 · submitted 2026-04-21 · 💻 cs.CL

Recognition: unknown

Tracing Relational Knowledge Recall in Large Language Models

Michael F\"arber, Nicholas Popovi\v{c}

Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:15 UTC · model grok-4.3

classification 💻 cs.CL

keywords relational knowledgelarge language modelsattention headslinear probesresidual streamrelation classificationfeature attribution

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The pith

Per-head attention contributions to the residual stream are strong features for linear classification of relations recalled by large language models.

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

This paper examines how large language models recall relational knowledge during text generation by identifying which internal representations best support linear classification of relations. It evaluates contributions from attention heads and MLPs, finding that per-head attention outputs to the residual stream are the strongest features for such probes. A reader would care because this provides a traceable signal for how models access and use stored facts, potentially aiding in understanding errors or hallucinations. The study also finds that probe success varies with relation specificity, entity connectedness, and signal distribution across heads. Token-level attribution further details the probe's decision process.

Core claim

We systematically evaluate different latent representations derived from attention head and MLP contributions, showing that per-head attention contributions to the residual stream are comparatively strong features for linear relation classification. Feature attribution analyses of the trained probes, as well as characteristics of the different relation types, reveal clear correlations between probe accuracy and relation specificity, entity connectedness, and how distributed the signal on which the probe relies is across attention heads. Finally, we show how token-level feature attribution of probe predictions can be used to reveal probe behavior in further detail.

What carries the argument

Per-head attention contributions to the residual stream as features for linear relation classification probes.

If this is right

Linear probes using per-head attention features achieve higher accuracy in classifying recalled relations than those using MLP or combined representations.
Probe performance increases with the specificity of the relation and the connectedness of subject and object entities.
The relevant signal for relation classification is spread across multiple attention heads rather than localized.
Token-level feature attribution can identify which specific tokens in the input most influence the probe's prediction of the relation.

Where Pith is reading between the lines

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

This probing method might be used to detect when a model is about to recall incorrect relations during generation.
Similar techniques could be applied to study recall of other knowledge types such as numerical or temporal facts.
Insights from head contributions could inform methods to enhance factual recall by strengthening certain attention patterns.

Load-bearing premise

Linear probes on per-head attention representations faithfully capture the model's internal relational recall mechanism rather than dataset artifacts or probe-specific patterns.

What would settle it

Observing whether ablating the per-head attention contributions to the residual stream causes the model to fail at recalling correct relations in generated text, or if the probes fail to generalize to unseen relation types or different model architectures.

Figures

Figures reproduced from arXiv: 2604.19934 by Michael F\"arber, Nicholas Popovi\v{c}.

**Figure 2.** Figure 2: Fill-in-the-blanks prompt used for evaluation. In red, we show the token position at which we probe for representations. The subject entity is marked in orange, while the object entity is shown in blue. It is not visible to the model during probing. Prompt Format Prior work studies relational knowledge recall using fixed phrase templates and often a question-answering setting to ensure that LLaMA-3.1/3.2 Q… view at source ↗

**Figure 3.** Figure 3: Correlations observed for probe precision (here for LLaMA-3.1 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Example of a prompt with inverted subject [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Example of a correct probe prediction for the relation type "crosses" in a [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Example of a correct probe prediction for the relation type "crosses" in a [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 7.** Figure 7: Overview of where attention and MLP contributions are measured in the LLaMA architecture. The [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗

**Figure 8.** Figure 8: Additional examples showing relevant information after tail entity mention, available due to fill-in-the [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Shown are both the aggregated TokenScore, as well as the average attention weights [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Examples of per-layer TokenScores. All examples were created with fill-in-the-blanks prompt for a [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗

**Figure 11.** Figure 11: Two examples of probe predictions, one of correct, the other of an incorrect prediction for the relation [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗

read the original abstract

We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for relation classification via linear probes. Prior work shows how attention heads and MLPs interact to resolve subject, predicate, and object, but it remains unclear which representations support faithful linear relation classification and why some relation types are easier to capture linearly than others. We systematically evaluate different latent representations derived from attention head and MLP contributions, showing that per-head attention contributions to the residual stream are comparatively strong features for linear relation classification. Feature attribution analyses of the trained probes, as well as characteristics of the different relation types, reveal clear correlations between probe accuracy and relation specificity, entity connectedness, and how distributed the signal on which the probe relies is across attention heads. Finally, we show how token-level feature attribution of probe predictions can be used to reveal probe behavior in further detail.

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.

Desk Editor's Note private letter to a colleague

Per-head attention residuals beat MLPs for linear relation probes and track specificity, but the link to actual model recall stays correlational.

read the letter

The paper shows that contributions from individual attention heads to the residual stream give better linear probe accuracy on relation classification than MLP outputs or other aggregated representations. It also reports that probe success correlates with relation specificity, entity connectedness, and how spread the signal is across heads, using feature attribution to inspect what the probes rely on. This is a clean, incremental extension of prior work on how transformers handle subject-predicate-object facts, and the systematic comparison plus the token-level attribution examples are the parts that feel most useful for follow-up work on editing or hallucination detection. The methods appear reproducible from the abstract description, with no obvious circularity in the reported numbers. The soft spot is the interpretation step. Linear probes succeeding does not show these representations are the ones the model uses for recall; they could be picking up dataset artifacts or distributed patterns that happen to align with the labels. The correlations with specificity and connectedness are interesting but do not rule out the probe exploiting those properties rather than tracing internal mechanisms. Without interventions like head ablation or activation patching, the claim that this traces recall remains observational. This is for interpretability researchers who already work with probing and want concrete numbers on representation choices. It is coherent and honest enough to deserve a serious referee, mainly to tighten the causal language and check controls for probe overfitting. I would send it to review.

Referee Report

2 major / 2 minor

Summary. The paper studies how LLMs recall relational knowledge during generation by evaluating latent representations from attention heads and MLPs for linear relation classification. It claims that per-head attention contributions to the residual stream are comparatively strong features for such probes, with feature attribution revealing correlations between probe accuracy and factors like relation specificity, entity connectedness, and signal distribution across heads. The work also applies token-level attribution to inspect probe predictions in detail.

Significance. If the central claim holds after addressing validation gaps, the work would advance mechanistic interpretability of LLMs by pinpointing which internal representations support relational recall and why some relations are more linearly accessible. The systematic comparison of representation types and the use of feature attribution to link accuracies to relation properties are positive elements that could guide future probing and editing techniques.

major comments (2)

[Abstract and experimental evaluation] The central claim that per-head attention contributions to the residual stream are comparatively strong features for linear relation classification (Abstract) rests on the unverified assumption that probe accuracies reflect the model's internal recall process. Without controls for dataset artifacts—such as relation specificity and entity connectedness that the paper itself correlates with accuracy—the probes may exploit data properties rather than model-derived signals, rendering the feature attribution analyses non-diagnostic of causal usage.
[Methods and experimental setup] The manuscript lacks sufficient detail on data splits, model variants, hyperparameter choices, and explicit controls against probe overfitting (e.g., cross-validation or adversarial baselines). This absence makes it impossible to verify that reported accuracies are not inflated by probe-specific patterns or distributed signals across heads, directly weakening support for the comparative strength of attention contributions.

minor comments (2)

The abstract would benefit from explicitly naming the LLMs, relation datasets, and number of relations evaluated to improve immediate assessability of scope and generalizability.
Notation for 'per-head attention contributions to the residual stream' should be defined more formally on first use, including how these are extracted from the forward pass.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important considerations for validating our probing results and ensuring methodological clarity. We address the major comments point by point below, providing clarifications and indicating planned revisions.

read point-by-point responses

Referee: [Abstract and experimental evaluation] The central claim that per-head attention contributions to the residual stream are comparatively strong features for linear relation classification (Abstract) rests on the unverified assumption that probe accuracies reflect the model's internal recall process. Without controls for dataset artifacts—such as relation specificity and entity connectedness that the paper itself correlates with accuracy—the probes may exploit data properties rather than model-derived signals, rendering the feature attribution analyses non-diagnostic of causal usage.

Authors: While we acknowledge that high probe accuracy does not by itself prove that the LLM internally uses these representations for relational recall, our analyses go beyond raw accuracy by employing feature attribution to identify the specific contributions from attention heads that the probes rely upon. The observed correlations with relation specificity, entity connectedness, and signal distribution are intended to explain variations in linear accessibility across relation types, which we view as a substantive finding rather than a confound. To further address concerns about dataset artifacts, we will incorporate additional controls such as adversarial baselines and stratified cross-validation in the revised manuscript to demonstrate that the probes capture model-derived signals. revision: partial
Referee: [Methods and experimental setup] The manuscript lacks sufficient detail on data splits, model variants, hyperparameter choices, and explicit controls against probe overfitting (e.g., cross-validation or adversarial baselines). This absence makes it impossible to verify that reported accuracies are not inflated by probe-specific patterns or distributed signals across heads, directly weakening support for the comparative strength of attention contributions.

Authors: We agree that the methods section would benefit from greater detail to facilitate reproducibility and verification. In the revised manuscript, we will expand the experimental setup to include: (1) precise descriptions of data splits, including how relations were divided into training and test sets while preserving relation types; (2) the full list of model variants evaluated; (3) hyperparameter settings for probe training, including regularization to prevent overfitting; and (4) results from cross-validation and adversarial baselines to confirm robustness against probe-specific patterns. These additions will strengthen the evidence for the comparative strength of per-head attention contributions. revision: yes

Circularity Check

0 steps flagged

Empirical probing study with no circular derivations or self-referential reductions

full rationale

The paper is an empirical investigation that trains linear probes on various LLM latent representations (attention head contributions to residual stream, MLP outputs) and measures classification accuracy on external relation datasets. Probe performance, feature attribution, and correlations with relation properties are reported as experimental outcomes. No equations, fitted parameters, or derivations are presented that reduce the reported accuracies or claims to the inputs by construction. The central claim follows directly from measured results on held-out data rather than tautological re-expression of fitted values or self-citation chains. The study is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard assumptions from the mechanistic interpretability literature rather than introducing new free parameters or entities.

axioms (1)

domain assumption Linear probes can extract causally relevant information from model activations about internal computations
Invoked when interpreting probe accuracy as evidence of suitable latent representations for relation classification.

pith-pipeline@v0.9.0 · 5440 in / 1019 out tokens · 36036 ms · 2026-05-10T02:15:27.913083+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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