Relation Before Entity: Deferred Commitment in Language Model Factual Recall
cs.CL
Quick verdict
Claims relation signals become generation-controlling before entity signals in 4 LMs; well-controlled patching study, yet no inferential statistics.
Summary
This paper asks whether relation-type information (e.g., capital-of) and entity-specific information (e.g., France→Paris) become causally active at the final-token position at the same depth during factual recall. Across four decoder-only models (Llama-3.2-3B, Llama-3-8B, Qwen2.5-3B, Phi-2) and eight fill-in-the-blank prompt families, four causal diagnostics are applied: transfer-curve activation patching, both-change competition, entity-token versus final-token patching, and linear steering. The central finding is a temporal asymmetry: relation transfer exceeds threshold 0.4 in middle layers 10-16 tested layers before entity transfer, an ordering that holds in all 16 model-threshold combinations for thresholds 0.2-0.5. Both-change crossovers and entity/final-token switch layers align with entity onset within 2 tested layers. Entity information is not absent early: entity-token patching succeeds at 90-100% in early layers while final-token patching stays near 2.4%, supporting deferred commitment (delayed routing) rather than delayed knowledge. Controls (wrong-entity, noise, self-patch, unrelated donors, random directions) argue against answer copying and generic perturbation. Main limitations: small uneven prompt banks (N=12-33; 83 records in Experiment 3), no confidence intervals or significance tests, coarse every-other-layer resolution, greedy decoding only, and models restricted to 3B-8B.
Review complete
Assessment at a glance
4
risk flags identified
● high 0
● medium 2
● low 2
5 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Relation-type information becomes generation-controlling at the final-token position 10-16 tested layers before entity-specific information at threshold 0.4, and the ordering holds in all 16 model-threshold combinations across thresholds 0.2-0.5.
- Entity information is available early at the entity-token position (90-100% patching success) but becomes generation-controlling at the final token only in late layers, so deferred commitment reflects delayed routing rather than delayed knowledge.
- Under direct relation-entity conflict (both-change competition), relation signals dominate middle layers and entity signals dominate late layers, with crossovers matching entity onsets within 2 tested layers.
- Mid-layer relation transfer reflects relational structure rather than donor-answer copying: relation-only transfer reaches 0.79-0.86 while copying stays at or below 0.114 at peak layers (wrong-entity control).
- Mean-difference steering directions reproduce the asymmetry: relation directions steer effectively in middle layers (0.62-0.77 hit rate), entity directions are weak mid-layer and strongest late.
Strengths
Robust ordering across models and thresholds
Table 1 and Appendix C (Table 5) show relation onset precedes entity onset in all 16 model-threshold combinations (thresholds 0.2-0.5) across four architectures of 28-36 layers, with gaps of 4-20 tested layers (10-16 at the primary threshold 0.4).
Clean double dissociation rules out the trivial alternative
Experiment 3 (Fig. 2, Table 3): entity-token patching transfers entity identity at 90-100% in early layers while final-token patching stays near 2.4%; the pattern reverses late (88-97% vs 2-4%), establishing that entity information is available without yet being generation-committed.
Extensive, well-chosen control suite
Convergent transitions across independent diagnostics
Honest positioning and scoping of claims
Weaknesses
No inferential statistics anywhere
Coarse temporal resolution inflates precision of gap claims
Small and uneven prompt banks
Model heterogeneity is downplayed
Sufficiency-only interventions
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Frequently asked questions
What does deferred entity commitment mean in this paper?
It is the finding that a language model already contains the specific answer information (e.g., Tokyo for the capital-of-Japan relation) at the token position of the input entity early in the network, but that information only starts controlling the generated token late in the network, after being routed to the final position. In other words, the knowledge is present early; its causal influence on the output is delayed.
Which language models were tested?
Four open-weight decoder-only transformer models: Llama-3.2-3B (28 layers), Llama-3-8B (32 layers), Qwen2.5-3B (36 layers), and Phi-2 (32 layers), spanning roughly 3B-8B parameters.
How is this different from Geva et al. 2023 on factual recall?
Geva et al. (2023) identified a three-stage information flow and showed, using attention-edge interventions, that relation and subject information reach the final token at different points. This paper adds quantified layer-wise onset gaps across four architectures and eight prompt families, direct activation-patching transfer measurements, a both-change competition that places relation and entity signals in conflict, and an explicit separation of early entity availability at the entity token from late final-token commitment.
Is code and data available for this paper?
Yes. Appendix F links a GitHub repository (github.com/divyanshddn146/deferred-entity-commitment) containing code and result files, and the prompt templates with record counts (12-33 per family) are listed in Appendix E.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures are available only as extracted caption text, so exact curve shapes, SEM ranges, and plotted points could not be independently verified.
- Item banks, hook implementations, and per-record CSV results live in the linked repository and were not verifiable from the paper text alone.
- Some appendix table values were parsed from flattened text; minor transcription risk cannot be excluded.
- The paper is a workshop version (Mechanistic Interpretability Workshop at ICML 2026), so review standards for full-length empirical papers may not fully apply.
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