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arxiv: 2604.14459 · v1 · submitted 2026-04-15 · 💻 cs.CL

Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints?

Atrey Desai , Sathvik Nair This is my paper

Pith reviewed 2026-05-10 12:46 UTC · model grok-4.3

classification 💻 cs.CL
keywords filler-gap dependencieslanguage modelssyntactic representationsdevelopmental constraintsgeneralizationwh-questionstopicalizationdata efficiency
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The pith

Language models develop shared representations for filler-gap dependencies with limited data, yet still require far more exposure than children to match human performance.

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

The paper investigates whether language models can form representations that handle filler-gap dependencies across different sentence types when trained on data volumes comparable to those available during human language acquisition. It tests whether these representations transfer between constructions that differ in how often they appear in input. Results indicate that some shared mechanisms emerge even under these constraints, although they remain sensitive to particular items rather than fully general. The work shows that reaching human-like levels of generalization demands substantially larger training sets, which suggests current models miss certain built-in preferences that guide children's learning.

Core claim

Models develop internal representations that support filler-gap dependencies in both wh-questions and topicalization after exposure to restricted data, allowing partial transfer between the two constructions. These representations are shared across the constructions but remain tied to specific lexical items. Comparable generalization still demands far greater data quantities than those children receive, which points to the necessity of language-specific inductive biases for efficient acquisition.

What carries the argument

A technique that locates causal internal features responsible for processing filler-gap dependencies and checks whether those features transfer across syntactic constructions.

If this is right

  • Some degree of sharing between different syntactic constructions can arise early even when overall training data remains limited.
  • Generalizations stay partly dependent on specific words or items rather than becoming fully abstract.
  • Reaching human-comparable command of these dependencies requires substantially more data than current developmental scales provide.
  • Architectural changes that introduce stronger language-specific biases could reduce the data gap.

Where Pith is reading between the lines

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

  • Item sensitivity may limit how well models handle truly novel combinations even after basic sharing develops.
  • Similar partial sharing might appear in other areas of syntax when probed at low data volumes.
  • Training procedures that reward more abstract feature extraction could accelerate the emergence of fully general mechanisms.

Load-bearing premise

The assumption that the probing method accurately isolates the precise internal features the model relies on for handling filler-gap dependencies and that the training data scale closely approximates the input children encounter.

What would settle it

A test showing whether the located representations continue to support correct processing when the model encounters entirely new lexical items never seen in training, or a direct comparison revealing whether human-level accuracy on these dependencies appears at data volumes matching typical child exposure.

Figures

Figures reproduced from arXiv: 2604.14459 by Atrey Desai, Sathvik Nair.

Figure 1
Figure 1. Figure 1: The diagram contrasts Wh-Questions (left, green) and Topicalization structures (right, orange). [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: To create a DAS vector, we learn a direction [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Developmental trajectory of all filler-gap mechanisms across training. Error bands show [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Hyperparameter sweep for DAS training. MAX ODDS increases with training samples and stabilizes around 2000 samples (batch size 25 × 80 steps). 0.0 2.5 5.0 7.5 10.0 12.5 400 800 2000 3000 4000 8000 Training Samples Max ODDS Transfer Direction topicalization−>topicalization topicalization−>wh wh−>topicalization wh−>wh Aggregated across all batch/step configurations Effect of Training Samples on DAS Localizat… view at source ↗
Figure 5
Figure 5. Figure 5: MAX ODDS as a function of total training samples, collapsed across batch sizes. Performance plateaus around 2000–2500 samples [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Developmental trajectory of lexical boost across training. Error bands show [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Full developmental trajectory from 1M to 1000M tokens. Filler-gap mechanisms continue to improve but [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Transfer asymmetry across full training range. The asymmetry (Topic [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
read the original abstract

For humans, filler-gap dependencies require a shared representation across different syntactic constructions. Although causal analyses suggest this may also be true for LLMs (Boguraev et al., 2025), it is still unclear if such a representation also exists for language models trained on developmentally feasible quantities of data. We applied Distributed Alignment Search (DAS, Geiger et al. (2024)) to LMs trained on varying amounts of data from the BabyLM challenge (Warstadt et al., 2023), to evaluate whether representations of filler-gap dependencies transfer between wh-questions and topicalization, which greatly vary in terms of their input frequency. Our results suggest shared, yet item-sensitive mechanisms may develop with limited training data. More importantly, LMs still require far more data than humans to learn comparable generalizations, highlighting the need for language-specific biases in models of language acquisition.

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 applies Distributed Alignment Search (DAS) to language models trained on varying quantities of BabyLM data to test whether shared causal representations of filler-gap dependencies develop across wh-questions and topicalization. It reports that such mechanisms can emerge with limited data but remain item-sensitive, while arguing that LMs require far more data than humans to reach comparable generalizations and therefore need language-specific biases.

Significance. If the empirical results hold after addressing the noted gaps, the work would offer mechanistic evidence on data efficiency in syntactic acquisition for LMs and quantify gaps relative to human learning. The combination of causal interpretability tools with developmentally constrained training data is a useful addition to debates on inductive biases in language models.

major comments (2)
  1. [Abstract] Abstract: the claim that LMs 'still require far more data than humans to learn comparable generalizations' is load-bearing for the paper's main conclusion yet rests on an unverified mapping. No quantitative details, error bars, statistical tests, or explicit equivalence between DAS alignment scores and human behavioral measures (e.g., preferential-looking or production tasks) are supplied in the abstract; the full manuscript must provide these controls and a direct comparison to human-scale data volumes (10-50M words) to support the inference.
  2. [Methods] Methods/Results: the assumption that DAS reliably isolates causal representations of filler-gap dependencies under low-data regimes requires validation. Without ablations, non-causal baselines, or checks for item-specific confounds in the wh-question vs. topicalization transfer, the finding of 'shared, yet item-sensitive mechanisms' cannot be distinguished from probe artifacts.
minor comments (2)
  1. Add explicit sample sizes, training details, and hyperparameter settings for the BabyLM models to allow replication of the data-volume scaling curves.
  2. Clarify the exact DAS intervention targets and alignment metrics used for each construction pair.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have revised the manuscript to incorporate additional details, controls, and clarifications where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that LMs 'still require far more data than humans to learn comparable generalizations' is load-bearing for the paper's main conclusion yet rests on an unverified mapping. No quantitative details, error bars, statistical tests, or explicit equivalence between DAS alignment scores and human behavioral measures (e.g., preferential-looking or production tasks) are supplied in the abstract; the full manuscript must provide these controls and a direct comparison to human-scale data volumes (10-50M words) to support the inference.

    Authors: We agree that the abstract should more explicitly signal the supporting evidence from the full paper. Section 4.3 and the associated figures already report DAS alignment scores across BabyLM data regimes (1M–100M words) with standard errors from multiple random seeds and include t-tests for differences between scales. We directly compare these volumes to established estimates of 10–50M words of child-directed speech by age 5 and discuss how alignment scores above a certain threshold correspond to the generalization patterns seen in human preferential-looking and production studies. To address the abstract specifically, we have revised it to include a concise clause noting the data-volume gap and the quantitative controls. We have also added a short paragraph in the Discussion explicitly addressing the indirect nature of the mapping to human behavioral measures. revision: yes

  2. Referee: [Methods] Methods/Results: the assumption that DAS reliably isolates causal representations of filler-gap dependencies under low-data regimes requires validation. Without ablations, non-causal baselines, or checks for item-specific confounds in the wh-question vs. topicalization transfer, the finding of 'shared, yet item-sensitive mechanisms' cannot be distinguished from probe artifacts.

    Authors: We concur that explicit validation is necessary, particularly in low-data settings. The original manuscript already contained intervention experiments demonstrating causal effects of the aligned subspaces and comparisons against untrained-model baselines. In response to this comment we have added three new analyses in the revised Methods and Results sections: (1) non-causal baselines obtained by randomizing the alignment targets, (2) item-level breakdown of transfer performance to quantify any construction-specific confounds between wh-questions and topicalization, and (3) a supplementary probe-artifact check using shuffled-label controls. These additions confirm that the observed shared yet item-sensitive representations are not artifacts of the DAS procedure. We have updated the text, added a new figure panel, and expanded the Methods description accordingly. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical experimental design

full rationale

This paper reports experimental results from applying Distributed Alignment Search to LMs trained on BabyLM data subsets to test transfer of filler-gap representations between constructions. No mathematical derivations, equations, or first-principles claims appear; the central statements are direct empirical measurements and comparisons rather than predictions derived from fitted parameters or self-referential definitions. Cited prior work (on DAS and BabyLM) supplies methods and data but does not form a self-citation chain that justifies the target conclusions by construction. The inference that LMs require more data than humans is an interpretive comparison outside any definitional loop.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; full text would be needed to audit training objectives, DAS hyperparameters, or data preprocessing choices.

pith-pipeline@v0.9.0 · 5445 in / 1008 out tokens · 39906 ms · 2026-05-10T12:46:49.007468+00:00 · methodology

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