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Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT

T0 review · 4 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Middle BERT layers best cluster verb-particle constructions, with category-specific peaks

desk verdict A modest layer-wise BERT probe of verb-particle constructions whose main claim is undercut by the absence of a surface-cue control. read the letter →

arxiv 2412.14670 v1 pith:FSYCMOAF submitted 2024-12-19 cs.CL cs.AI

Hassane Kissane , Achim Schilling , Patrick Krauss This is my paper · ORCID
classification cs.CLcs.AI
keywords verb-particleconstructionsBERTconstructiongrammargeneralizeddiscriminationvaluemulti-dimensionalscalinghiddenstateslayer-wiseanalysisphrasalverbs
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether a transformer language model such as BERT internally splits verb-particle constructions according to their grammatical type, and where in the network that split happens. It reports that the hidden states of the verb token form distinct clusters that are strongest in the middle layers (3–4 in BERT-base, 6–7 in construction-trained CxG-BERT), and that the separation strength varies by verb category. If correct, this means BERT does not encode linguistic constructions uniformly across its layers, and that construction grammar's distinction between lexical phrasal verbs and compositional verb-preposition combinations has a real correlate in the geometry of embeddings. The finding also suggests a practical guide for where to probe or intervene in such models.

What carries the argument

The load-bearing tool is the generalized discrimination value (GDV), a scalar that measures how separated pre-defined classes are in a high-dimensional space. For each layer, the authors take the 768-dimensional hidden state of the verb token, z-score each dimension, and compute the mean intra-class distance minus the mean inter-class distance, normalized by dimensionality; negative values indicate clustering, and -1 is already very strong separation. They pair this with multi-dimensional scaling (MDS) to project the same embeddings into two dimensions for visual inspection. The GDV is what turns the raw embeddings into a quantitative claim about layer-specific capture of structure, and its sign convention (negative means separated) is what lets the paper read the middle-layer trough as a peak of representational strength.

What would settle it

Replace the particle token in each test sentence with a randomly chosen word from the same corpus and recompute the GDV on the verb token's hidden states; if strong negative GDV persists, the separation does not depend on the construction. Alternatively, permute the construction labels among the embedded verbs and measure how often the observed GDV exceeds what is obtained under random label assignment; if it is not an extreme outlier, the clustering is not statistically meaningful.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that BERT's internal representations of verb-particle constructions are organized by construction type, and that this organization peaks in the middle layers. Using the generalized discrimination value (GDV) on the hidden-state vectors of the verb token, the authors find that all three verb groups (agree, come, give) show increasingly negative GDV from early to middle layers, with give reaching the strongest separation (-0.305 at layer 3 in BERT-base) and the other two groups peaking at slightly weaker values around -0.26 and -0.24. The construction-trained CxG-BERT shows the same trend with a later peak (layers 6–7). The authors interpret the strong clustering of give as evidence that idiomatic phrasal verbs are represented as unified lexical units; the weak clustering of come aligns with its compositional, non-idiomatic character; and the unexpected clustering of agree suggests that even prepositional verbs receive construction-specific representations. Thus the paper claims that the model captures constructional knowledge in a layer-dependent, category-dependent way.

Load-bearing premise

The argument treats a negative GDV for verb-token embeddings as direct evidence that the model captures syntactic or constructional structure, yet no null distribution or permutation test is supplied; the observed clustering could in principle be produced by surface cues such as the immediately following particle, without the model having any abstract knowledge of constructions.

Editorial extensions

If this is right

  • If the layer dependence is correct, the middle layers (3–4 for BERT-base, 6–7 for CxG-BERT) are the most promising places to extract construction-level features for downstream tasks.
  • The category-specific differences in GDV imply that conclusions about a model's syntactic knowledge should be stated per construction type, not globally.
  • The weakening of clustering in late layers, with more outliers, suggests that final-layer representations are the least reliable for reading constructional information.
  • The shift of the peak toward layers 6–7 in CxG-BERT indicates that additional training on construction examples can relocate where constructional knowledge is encoded.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension would be to test whether the verb-token clustering survives when the particle token is masked or replaced; if it does, the separation reflects the verb's wider sentential context rather than the specific construction, which would alter the construction-grammar interpretation.
  • Computing GDV on verb-plus-particle token pairs, rather than the verb alone, would directly test whether the model treats the combination as a single lexical unit and could reveal whether the isolated verb clusters are driven by the particle's surface presence.
  • Permutation-based significance testing on the GDV values would provide a null distribution for no constructional structure, allowing direct statistical comparison of the layer peaks and a common scale for future model comparisons.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper analyzes how BERT and CxG-BERT represent English verb-particle constructions (VPCs) by extracting target-verb embeddings from each layer of the two models, projecting them with multidimensional scaling (MDS), and computing the Generalized Discrimination Value (GDV) to quantify cluster separability. The dataset consists of 995 naturally occurring sentences from the British National Corpus, covering 11 VPC types across three verb categories ('agree', 'come', 'give'). The results show that GDV-based clustering becomes strongest in middle layers (layers 3–4 in BERT-base, layers 6–7 in CxG-BERT), with the 'give' and 'come' categories exhibiting stronger within-category separation than 'agree'. The authors interpret this as evidence that BERT's middle layers capture syntactic structures and that the model treats phrasal verbs as lexical units, in line with construction grammar.

Significance. If the central pattern were established with proper controls, the paper would provide a useful layer-resolved characterization of how a transformer represents multi-word constructions, speaking directly to BERTology and to construction-grammar interpretations of neural models. The study is among the first to target verb-particle constructions specifically, and it uses a parameter-free geometric measure (GDV) on embeddings from externally trained, off-the-shelf models, which is a methodological strength. However, the central claim currently rests on a single descriptive statistic whose interpretation is confounded by the immediate lexical context, and the paper provides no uncertainty quantification or null baseline. These issues are fixable but are load-bearing, so the paper is not yet ready for acceptance.

major comments (4)
  1. [§2.4, §3] Section 2.4 defines the GDV and Section 3 reports GDV values for the target-verb embeddings, but in every input sentence the target verb is immediately followed by its particle (e.g., 'give up', 'give out', 'give in'; 'agree on', 'agree with'). Since BERT's contextualized embeddings are computed with bidirectional attention, the verb's hidden state at any layer is directly conditioned on the adjacent particle token. The within-category separation of 'give' constructions, for example, could therefore be trivially driven by the orthographic identity of the following particle ('up' vs 'out' vs 'in') rather than by any construction-level syntactic or semantic representation. No null distribution, permutation test, or control condition that removes or randomizes the local particle cue is provided. Without such a baseline, the central claim that BERT's middle layers 'capture syntactic structures' of VPCs is not established.
  2. [§3] Section 3 presents GDV values as point estimates (e.g., 'give' reaching -0.305 at layer 3 vs -0.288 at layer 4; 'come' -0.236 at layer 3 vs -0.191 at layer 4), but the dataset has only 93–100 sentences per construction (Section 2.1) and no error bars, confidence intervals, or significance tests are reported. The abstract's phrase 'significant variability' is descriptive only. The authors should provide bootstrap confidence intervals over sentences and a permutation null distribution (e.g., randomly permuting the particle labels) to determine whether the layer- and category-dependent differences exceed chance.
  3. [§4] The interpretation in Section 4 that strong GDV clustering reflects 'lexical unity of phrasal verbs' or 'syntactic structures' is an over-reach relative to the measure. GDV quantifies Euclidean separability of the target-verb embeddings by class label, but separability by the following particle is compatible with the model encoding simple lexical co-occurrence statistics rather than constructional knowledge. A direct syntactic probe (e.g., a classifier trained on the embeddings to predict argument structure, or a controlled test set that manipulates the particle while holding context constant) is needed to support the constructional interpretation. At minimum, the claims should be scaled back to what the measure shows: that the verb embeddings are separable by construction type.
  4. [§3] Figure 3 and the text in Section 3 report that the GDV computed over all input data is already -0.358 at layer 1 in the original BERT, which is more negative than the strongest within-category values. This suggests that global separation into the three verb groups is present from the earliest layer and may dominate the metric, yet the paper does not analyze how the overall clustering relates to the category-specific GDV curves. For example, the weaker within-category clustering of 'agree' may simply reflect that the verb 'agree' is always the same token with less distinctive following prepositions. The authors should report and discuss the relationship between the overall and within-category GDV values, for instance by conditioning on verb identity.
minor comments (7)
  1. [Figures 1–3] Figures 1–3 are referenced but not embedded in the manuscript text, so the MDS projections cannot be inspected; the captions alone are insufficient for verification.
  2. [Throughout] The manuscript contains numerous typos and grammatical errors (e.g., 'showing week clustering', 'the string clustering recorded', 'its meaning is emerged from its form'); a thorough proofreading pass is needed.
  3. [§2.2] CxG-BERT is described only as having 'extra-training using sentences instantiating constructions'; the training details (fine-tuning vs continued pre-training, data size, hyperparameters) should be specified.
  4. [General] The paper does not state whether the analysis code or dataset are publicly available; providing them would strengthen the empirical claims.
  5. [Tables] Table numbering is inconsistent (Table 2 appears before Table 1, and the data-cleaning table is numbered Table 12); renumber the tables sequentially.
  6. [References] The reference to Mickus et al. appears as 2019 in the text but 2020 in the bibliography; please ensure all in-text citations match the reference list.
  7. [§2.2] Section 2.2 notes that the Wikipedia portion of BERT's and CxG-BERT's training data 'might be not standard from a pure linguist view'; the paper should discuss the implications of this limitation for the linguistic generalizability of the findings.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study is an observational probe of externally trained BERT embeddings; its GDV metric is re-derived in the paper and its category outcomes are not forced by construction.

full rationale

The paper's derivation chain is an empirical measurement, not a fitted model. It extracts verb-token embeddings from externally pre-trained BERT and CxG-BERT (Sections 2.2 and 2.5), defines the GDV cluster-separability statistic explicitly in Section 2.4, and reports the obtained per-layer values in Section 3. No parameter is fitted to the BERT representations, and no target result is used to define the labels: the construction categories (agree/come/give) are stated in advance from linguistic descriptions (Sections 1.5 and Tables 3-11), and the GDV is applied to the resulting embeddings. The authors' self-citations (Schilling et al. 2021a,b; Krauss et al. 2021; Surendra et al. 2023) introduce MDS/GDV as visualization and measurement tools, but the GDV formula is re-stated completely in the paper and is parameter-free, so the argument does not depend on accepting an unverified prior result as a black box. The central finding (middle-layer cluster separation, with category differences) is not equivalent to the inputs by construction: the paper reports a non-trivial pattern, and even notes that its theoretical expectation for 'agree' was not supported ('we could not find any evidence to support this assumption'), which shows the measurement could have come out differently. Concerns about the absence of a null distribution or a surface-context control bear on whether the observed separation reflects genuine constructional representation versus immediate lexical co-occurrence, but that is a validity or correctness issue, not a circularity in the derivation chain.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no fitted parameters and no invented entities. Its central claim rests on domain assumptions about the validity of GDV as a linguistic probe, the correctness of the construction grammar taxonomy, and the representativeness of the BNC sample. These assumptions are not separately validated.

assumptions (5)
  • domain assumption The GDV of z-scored embeddings is a valid measure of linguistic class separability.
    Invoked in Section 2.4 and Section 3; the paper interprets negative GDV as evidence that BERT clusters constructions, but provides no validation against a null model or an external linguistic measure.
  • domain assumption The construction grammar taxonomy, phrasal verbs as lexical units and prepositional verbs as syntactic composites, is the correct framework for interpreting embeddings.
    Stated in Sections 1.5 and 1.6 and used in the Discussion; if this taxonomy is wrong, the central interpretation collapses.
  • domain assumption The 995 BNC samples with 10-token contexts are representative of each construction.
    The dataset is described in Section 2.1 with no coverage analysis or error analysis; all conclusions depend on these samples.
  • standard math Standard BERT and CxG-BERT pretrained models are used correctly.
    Described in Section 2.2; the paper relies on the transformers library and pretrained weights without fine-tuning, which is standard practice.
  • standard math Euclidean distance and z-scoring in 768 dimensions behave as expected for cluster analysis.
    The GDV definition in Section 2.4 assumes these standard operations are valid; no correction for high-dimensional concentration is discussed.

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Pith. "Pith review of Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT." pith.science (2026). https://pith.science/paper/FSYCMOAF

@misc{pith2026241214670,
  author       = {Pith},
  title        = {Pith review of: Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FSYCMOAF}},
  note         = {Machine review of arXiv:2412.14670}
}
read the original abstract

This study investigates the internal representations of verb-particle combinations within transformer-based large language models (LLMs), specifically examining how these models capture lexical and syntactic nuances at different neural network layers. Employing the BERT architecture, we analyse the representational efficacy of its layers for various verb-particle constructions such as 'agree on', 'come back', and 'give up'. Our methodology includes a detailed dataset preparation from the British National Corpus, followed by extensive model training and output analysis through techniques like multi-dimensional scaling (MDS) and generalized discrimination value (GDV) calculations. Results show that BERT's middle layers most effectively capture syntactic structures, with significant variability in representational accuracy across different verb categories. These findings challenge the conventional uniformity assumed in neural network processing of linguistic elements and suggest a complex interplay between network architecture and linguistic representation. Our research contributes to a better understanding of how deep learning models comprehend and process language, offering insights into the potential and limitations of current neural approaches to linguistic analysis. This study not only advances our knowledge in computational linguistics but also prompts further research into optimizing neural architectures for enhanced linguistic precision.

Figures

Figures reproduced from arXiv: 2412.14670 by the authors.

Figure 1
Figure 1. Early layers results of neural network testing and projection onto a 2- dimensional plane using MDS, with colour coding according to subsequent [PITH_FULL_IMAGE:figures/full_fig_p026_1.png] view at source ↗

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities

    q-bio.NC 2025-06 conditional novelty 4.0 of 10

    EEG and MEG responses during audiobook listening scale with BERT's word predictability: more predictable nouns evoke smaller N400-like responses and altered pre-onset activity.

  2. Probing Internal Representations of Multi-Word Verbs in Large Language Models

    cs.CL 2025-02 reject novelty 4.0 of 10

    A study claims BERT stores phrasal and prepositional verbs in a non-linearly separable way, but its own linear classifiers separate the two categories almost perfectly.

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages · cited by 2 Pith papers

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    Methods: 2.1. Dataset creation and pre-processing: We collected natural language text data for training our model from the British national corpus, with queries to search the target construction (target verb + target particle) preceded by 10 tokens and followed by the other 10 tokens. The complete text data consists of a total of 995 samples. The number o...

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    Discussion: Linguistic explanations: Considering the construction grammar framework, and the theoretical analysis of the phrasal verbs and verb-preposition combinations. We discuss the given results in terms of two approaches, the first is the lexical unity of phrasal verbs and the non- unity in verb- preposition combinations. The second is the representa...

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    Bibliography: - Arbib, M. A., & Lee, J. (2008). Describing visual scenes: Towards a Neurolinguistics Based on Construction Grammar. Brain Research, 1225, 146- 162. https://doi.org/10.1016/j.brainres.2008.04.075 - Belinkov, Y. 2022. Probing Classifiers: Promises, Shortcomings, and Advances. Computational Linguistics, 48(1):207–219. - Belinkov, Y., Durrani,...

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