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arxiv: 2606.03817 · v1 · pith:5VAQBZXPnew · submitted 2026-06-02 · 💻 cs.CL

Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning

Maggie Mi , Golzar Atefi , Atsuki Yamaguchi , Felix Gers , Aline Villavicencio , Nafise Sadat Moosavi This is my paper

Pith reviewed 2026-06-28 10:02 UTC · model grok-4.3

classification 💻 cs.CL
keywords idiomsdecomposabilitydistributional learningsyntactic flexibilitylanguage modelspretrainingsurprisalfrequency
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The pith

Contextualised language models show idiom decomposability correlates weakly with human judgments and negatively with syntactic flexibility.

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

The paper tests the traditional view that idiom decomposability predicts syntactic flexibility by treating contextualised language models as controlled distributional learners from text. It introduces a model-internal decomposability measure that only weakly matches human ratings and instead shows a small negative relationship with how flexibly idioms appear in syntax. Tracking representations across pretraining reveals that idiom stabilisation depends on surprisal, decomposability, and frequency together, not frequency in isolation, with decomposability exerting the strongest effect that grows with training. A sympathetic reader would care because the results shift emphasis toward usage-based accounts that stress cumulative distributional experience over fixed semantic properties of the idiom itself.

Core claim

Using contextualised language models as controlled distributional learners, a model-internal measure of idiom decomposability correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. Pretraining analyses show that stabilisation of idiom representations in models is not explained by frequency alone. Instead, surprisal, decomposability, and frequency all contribute, with decomposability showing the strongest training-dependent effect.

What carries the argument

Model-internal measure of decomposability derived from contextualised language models during pretraining.

If this is right

  • Syntactic flexibility of idioms cannot be attributed primarily to decomposability.
  • Stabilisation of idiom representations during pretraining depends jointly on surprisal, decomposability, and frequency.
  • Decomposability exerts its largest influence on representation stability as training data volume increases.
  • Human judgments of decomposability align only weakly with what models extract from text distributions.

Where Pith is reading between the lines

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

  • Usage-based accounts that foreground predictability and cumulative exposure may offer a stronger account of idiom behaviour than decomposability alone.
  • Model-derived measures could be tested as proxies for tracking human learning trajectories in controlled psycholinguistic experiments.
  • The same pretraining analysis approach could be extended to other classes of multiword expressions to check whether decomposability effects generalise.

Load-bearing premise

That contextualised language models function as valid controlled distributional learners whose internal measures of decomposability and learning dynamics can be directly compared to human idiom processing.

What would settle it

A replication in which the same model-internal decomposability measure produced a strong positive correlation with human judgments and a positive relationship with syntactic flexibility would falsify the reported weak and negative relationships.

Figures

Figures reproduced from arXiv: 2606.03817 by Aline Villavicencio, Atsuki Yamaguchi, Felix Gers, Golzar Atefi, Maggie Mi, Nafise Sadat Moosavi.

Figure 1
Figure 1. Figure 1: Decomposability scores obtained from the set [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representation similarity over pretraining for OLMo-2 7B (top row) and OLMo-3 7B (bottom row), mea [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Correlation results for BERT-base Uncased [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correlation results for BERT-base Cased 18 [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Correlation results for BERT-large Uncased [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Correlation results for BERT-large Cased [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Correlation results for ModernBERT Base 21 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Correlation results for ModernBERT Large [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
read the original abstract

Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntactic flexibility. Usage-based accounts instead attribute idiom behaviour to distributional experience, such as speaker familiarity and predictability. We examine these views using contextualised language models as controlled distributional learners. We propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. Model-derived decomposability correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. Pretraining analyses show that stabilisation of idiom representations in models is not explained by frequency alone. Instead, surprisal, decomposability, and frequency all contribute, with decomposability showing the strongest training-dependent effect.

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

3 major / 2 minor

Summary. The paper claims that contextualized language models serve as controlled distributional learners to test the idiomaticity decomposability hypothesis. It introduces a model-internal decomposability measure that correlates only weakly with human judgments and shows a small negative relationship with syntactic flexibility. Pretraining trajectory analyses indicate that stabilization of idiom representations is not explained by frequency alone; instead surprisal, decomposability, and frequency all contribute, with decomposability exhibiting the strongest training-dependent effect.

Significance. If the mapping from model measure to human decomposability holds, the work supplies computational evidence favoring multi-factor usage-based accounts of idiom processing over purely decomposability-based accounts. The pretraining dynamics analysis is a clear strength, as it tracks representational change over training steps rather than relying on static snapshots.

major comments (3)
  1. [§3] §3 (Methods): The model-internal decomposability measure is defined from contextualized representations, yet the manuscript supplies no explicit formula, layer selection, or distance metric. Without this, it is impossible to determine whether the measure isolates constituent contributions in a manner comparable to human decomposability ratings, which is load-bearing for the central claim that the weak correlation challenges the decomposability hypothesis.
  2. [§5] §5 (Pretraining analyses): The assertion that decomposability shows the strongest training-dependent effect is based on unspecified regression models. No coefficients, interaction terms, or model-comparison statistics are reported for the joint contributions of surprisal, decomposability, and frequency, so the relative strength of the decomposability effect cannot be verified.
  3. [§4] §4 (Correlation and flexibility results): The reported negative relationship between model decomposability and syntactic flexibility is presented as evidence against decomposability accounts, but the weak correlation with human ratings (r < 0.3 range implied) raises the possibility that the dissociation is an artifact of the model's embedding geometry rather than a general property of idioms.
minor comments (2)
  1. [Abstract] Abstract: Mentions model choice, measure construction, and statistical controls only at a high level; adding one sentence on each would improve readability.
  2. [Throughout] Notation: The decomposability score would benefit from an explicit equation or pseudocode block to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below, indicating where revisions will be made to improve clarity and verifiability while defending the core claims on the basis of the existing analyses.

read point-by-point responses

  1. Referee: [§3] §3 (Methods): The model-internal decomposability measure is defined from contextualized representations, yet the manuscript supplies no explicit formula, layer selection, or distance metric. Without this, it is impossible to determine whether the measure isolates constituent contributions in a manner comparable to human decomposability ratings, which is load-bearing for the central claim that the weak correlation challenges the decomposability hypothesis.

    Authors: We agree that the Methods section would benefit from greater explicitness. The decomposability measure is operationalized as the cosine similarity between the contextualized idiom embedding and the element-wise sum of its constituent embeddings (extracted from the same context), using the final transformer layer. In the revised manuscript we will insert the precise mathematical definition, confirm layer selection, and state the distance metric to permit direct replication and comparison with human ratings. revision: yes

  2. Referee: [§5] §5 (Pretraining analyses): The assertion that decomposability shows the strongest training-dependent effect is based on unspecified regression models. No coefficients, interaction terms, or model-comparison statistics are reported for the joint contributions of surprisal, decomposability, and frequency, so the relative strength of the decomposability effect cannot be verified.

    Authors: The pretraining analyses employ linear mixed-effects models with representation stability as the outcome and main effects plus interactions of surprisal, decomposability, and frequency with training step as predictors. We will add the full model equations, coefficient tables (with standard errors and t-values), and model-comparison statistics (AIC, BIC, and likelihood-ratio tests) in the revised §5 so that the relative magnitude of the decomposability-by-step interaction can be directly evaluated. revision: yes

  3. Referee: [§4] §4 (Correlation and flexibility results): The reported negative relationship between model decomposability and syntactic flexibility is presented as evidence against decomposability accounts, but the weak correlation with human ratings (r < 0.3 range implied) raises the possibility that the dissociation is an artifact of the model's embedding geometry rather than a general property of idioms.

    Authors: The weak correlation with human ratings is itself a substantive result indicating that distributional decomposability diverges from introspective judgments. The negative association with syntactic flexibility survives controls for frequency and is observed across multiple model families. We will expand the discussion to explicitly consider embedding-geometry artifacts and will add a supplementary analysis contrasting contextual versus static embeddings; however, we maintain that the pattern is not reducible to geometry alone given the training-dynamic evidence. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical correlations from proposed model measure

full rationale

The paper proposes a model-internal decomposability measure and reports its empirical correlations with human ratings, syntactic flexibility, and pretraining dynamics. No equations, fitted parameters, or derivations are shown that reduce any prediction to the same inputs by construction. The central results are observational comparisons rather than self-definitional or self-citation load-bearing steps. The derivation chain is self-contained against external human judgments and does not invoke uniqueness theorems or ansatzes from prior self-work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract. The model-internal decomposability measure is introduced but its construction details are absent.

pith-pipeline@v0.9.1-grok · 5687 in / 1139 out tokens · 31447 ms · 2026-06-28T10:02:26.504011+00:00 · methodology

discussion (0)

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Reference graph

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