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arxiv: 2605.08837 · v1 · submitted 2026-05-09 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans

Odysseas S. Chlapanis , Orfeas Menis Mastromichalakis , Christos H. Papadimitriou
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:12 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords LLMsabstract conceptsgroundingproperty generationcognitive sciencesparse autoencoders
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The pith

LLMs anchor abstract concepts through word associations rather than human-like emotional and experiential grounding.

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

The paper replicates cognitive science property-generation experiments on 21 frontier and open-weight LLMs to test how they ground abstract concepts such as justice or theory. Models generate property lists dominated by word associations while producing fewer features tied to emotions and internal states, yielding low similarity to human data. When the same models rate concepts directly on grounding categories, their responses align more closely with humans and improve as model size increases. Internal inspection via sparse autoencoders finds features linked to sensorimotor and social dimensions, indicating that relevant information is encoded but not activated during free generation.

Core claim

LLMs recover grounding dimensions when explicitly queried but do not recruit them in a human-like way when words are generated freely for abstract concepts.

What carries the argument

The property-generation task, in which participants list properties associated with abstract concepts to reveal their grounding sources.

Load-bearing premise

The property-generation task and rating scales from cognitive science measure the same underlying grounding process in LLMs as in humans, allowing direct numerical comparison of their outputs.

What would settle it

A replication of the property-generation task in which models receive explicit instructions to include emotional and internal-state properties, followed by a check of whether correlations with human lists rise substantially.

Figures

Figures reproduced from arXiv: 2605.08837 by Christos H. Papadimitriou, Odysseas S. Chlapanis, Orfeas Menis Mastromichalakis.

Figure 1
Figure 1. Figure 1: Per-word property-category frequency distributions for three frontier LLMs vs. the human [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean r for all LLMs vs. the human ceiling on Experiment 1 [Harpaintner et al., 2018]. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pearson-r correlation heatmaps for three representative frontier models and humans. larger models and stronger general-purpose systems do not show reliably higher human alignment, suggesting that the mismatch is not simply a capability bottleneck that shrinks with model size. Inter-model correlations reveal a complementary pattern. As shown in Figure 3a, models correlate substantially more with one another… view at source ↗
Figure 4
Figure 4. Figure 4: Mean r for all LLMs vs. the human ceiling on the rating experiment [Troche et al., 2017]. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Percentage of mean r for all LLMs and SAE features vs. the estimated human ceiling mean [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Property-frequency boxplots for all 21 LLMs, sorted by Mean r. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Mean r for all LLMs vs. the human ceiling on Experiment 2 [Kelly et al., 2024]. Parameter [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Experiment 2 properties correlation heatmap for the concrete word subset. The correlation [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Mean Pearson r vs. number of runs averaged, on Experiment 1, for GEMMA-3-4B-IT coded by Gemini-2.5-Flash-Lite. Solid line: mean over 50 random subsets at each subset size; band: ±1 std across subsets; dashed: 100-run asymptote; dotted: the 10-run cadence used by the Experiment 1 leaderboard. E Variance and uncertainty in Experiment 1 The Experiment 1 leaderboard reports each model’s Mean Pearson r as the p… view at source ↗
Figure 10
Figure 10. Figure 10: Property generation prompt for Experiment 1 Harpaintner et al. [2018], reproduced [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Property generation prompt used for Experiment 2 Kelly et al. [2024], reproduced verbatim. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Coding prompt for Experiment 1. Categories are listed verbatim from Harpaintner et al. [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Rating prompt for the Troche benchmark, shown for the [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Number of SAE features per layer identified with our detection algorithm in Gemma-3-4B. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Boxplot for steered model with sensory (top) and internal (bottom) features. [PITH_FULL_IMAGE:figures/full_fig_p026_15.png] view at source ↗
read the original abstract

Abstract concepts - justice, theory, availability - have no single perceivable referent; in the human brain, their meaning emerges from a web of experiences, affect, and social context. Do large language models (LLMs) ground abstract concepts in a similar way? We study this by replicating property-generation experiments from cognitive science on 21 frontier and open-weight LLMs. Across models and experiments, we find a consistent pattern: when compared to humans, models rely too heavily on word associations, and underproduce properties tied to emotion and internal states. This yields a large and consistent grounding gap: no model exceeds a Pearson correlation r=0.37 with human responses, compared to a human-to-human ceiling above r=0.9. To better interpret this gap, we also replicate a rating experiment on grounding categories and find that here LLMs align more closely with human judgment, and alignment improves as models get larger. We then use sparse autoencoders (SAEs) to inspect whether this information is also reflected in the models' internal features, and we do identify features connected to grounding dimensions such as "sensorimotor" and "social". These findings suggest that current LLMs can recover grounding dimensions when explicitly queried, but do not recruit them in a human-like way when words are generated freely.

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 replicates property-generation experiments from cognitive science on 21 LLMs to compare how they ground abstract concepts (e.g., justice, theory) with humans. It reports consistent low Pearson correlations (no model exceeds r=0.37) versus human-human ceiling >0.9, with LLMs over-relying on word associations and under-producing emotion/internal-state properties. A rating task shows better alignment that scales with model size, and SAE analysis identifies internal features tied to grounding dimensions such as sensorimotor and social. The authors conclude LLMs recover grounding info when explicitly queried but do not recruit it human-like in free generation.

Significance. If the patterns are robust, the work offers a large-scale empirical demonstration of systematic differences in abstract-concept representation between LLMs and humans, with implications for interpretability, cognitive modeling, and AI safety. Strengths include the breadth across 21 models, dual behavioral paradigms, and the SAE probe of internal representations. The significance hinges on whether the property-generation outputs provide commensurable measures of grounding.

major comments (3)
  1. [Methods] Methods section: the replication of the property-generation task omits exact prompt wording, response coding scheme (how properties are classified as word-association vs. emotion/internal-state), per-model sample sizes, exclusion criteria, and statistical controls. These omissions prevent full evaluation of the r=0.37 ceiling and the frequency-distribution comparisons that support the grounding-gap claim.
  2. [Results (property-generation experiment)] Results (property-generation experiment): the central claim that LLMs under-recruit grounding dimensions assumes the free-generation task elicits directly comparable conceptual-structure measures in LLMs and humans. Because LLMs operate via next-token statistics, output-style or prompt-interpretation differences could produce the observed gap; a concrete control (e.g., length-matched or format-constrained generation) is needed to isolate grounding from response-regime effects.
  3. [SAE analysis] SAE analysis section: the identification of features linked to 'sensorimotor' and 'social' dimensions should report quantitative activation statistics during the generation versus rating regimes and the precise mapping criteria used to label features; without these, it is unclear whether the internal representations are recruited equivalently in the free-generation setting that drives the main claim.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'two experiment types' should explicitly name the property-generation and rating tasks to improve clarity for readers unfamiliar with the cognitive-science paradigm.
  2. [Figures] Figures: correlation plots should include human-human baseline lines and confidence intervals to allow immediate visual assessment of the reported gap.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major point below, indicating the revisions we will make to improve the manuscript's clarity, reproducibility, and rigor.

read point-by-point responses

  1. Referee: [Methods] Methods section: the replication of the property-generation task omits exact prompt wording, response coding scheme (how properties are classified as word-association vs. emotion/internal-state), per-model sample sizes, exclusion criteria, and statistical controls. These omissions prevent full evaluation of the r=0.37 ceiling and the frequency-distribution comparisons that support the grounding-gap claim.

    Authors: We agree that additional methodological detail is necessary for full reproducibility and evaluation. In the revised manuscript, we will include the exact prompt templates used across models, a detailed description of the property coding scheme with explicit criteria and examples for classifying properties (e.g., word-association vs. emotion/internal-state), per-model sample sizes, exclusion criteria applied to responses, and any statistical controls used in the Pearson correlations and frequency analyses. revision: yes

  2. Referee: [Results (property-generation experiment)] Results (property-generation experiment): the central claim that LLMs under-recruit grounding dimensions assumes the free-generation task elicits directly comparable conceptual-structure measures in LLMs and humans. Because LLMs operate via next-token statistics, output-style or prompt-interpretation differences could produce the observed gap; a concrete control (e.g., length-matched or format-constrained generation) is needed to isolate grounding from response-regime effects.

    Authors: We acknowledge the potential for response-regime confounds given LLMs' next-token prediction mechanism. Our design deliberately replicates the free-generation protocol from human cognitive science studies to enable direct comparison, and the substantially higher alignment in the rating task (using identical models but a different output format) indicates the gap is not solely attributable to generation style. We will expand the discussion section to explicitly address this limitation and potential alternative explanations. However, adding new constrained-generation controls would require substantial additional experiments beyond the current scope; we therefore treat this as a point for future work rather than a revision to the present analyses. revision: partial

  3. Referee: [SAE analysis] SAE analysis section: the identification of features linked to 'sensorimotor' and 'social' dimensions should report quantitative activation statistics during the generation versus rating regimes and the precise mapping criteria used to label features; without these, it is unclear whether the internal representations are recruited equivalently in the free-generation setting that drives the main claim.

    Authors: We appreciate this request for greater quantitative detail. In the revised SAE analysis section, we will report activation statistics (including mean activations, distributions, and comparisons) for the identified features across both the generation and rating regimes. We will also provide a precise description of the mapping criteria, including how features were selected and labeled based on top-activating examples, correlations with grounding dimensions, and any thresholding or validation steps used. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical measurements

full rationale

The paper reports direct experimental replications of property-generation tasks and rating scales from cognitive science, followed by Pearson correlations (r ≤ 0.37 vs. human ceiling > 0.9) and SAE feature inspection. No equations, fitted parameters, or derivations appear; results are computed from model outputs and human data without any step that reduces the claimed gap to a self-definition, renamed input, or self-citation chain. The central findings rest on observable frequency distributions and internal activations rather than any load-bearing assumption that is justified only by prior author work or by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper rests on the validity of prior cognitive-science tasks and standard statistical comparison methods; no free parameters are introduced to define the gap itself.

axioms (2)
  • domain assumption Property-generation responses from humans and LLMs can be directly compared via Pearson correlation as measures of shared grounding.
    Invoked when reporting r=0.37 versus human ceiling >0.9
  • standard math Standard assumptions of Pearson correlation (linearity, normality of residuals) hold for the property-count data.
    Used for all reported correlations
invented entities (1)
  • grounding gap no independent evidence
    purpose: Label for the observed systematic difference in property generation between LLMs and humans
    Introduced in the abstract to summarize the main empirical discrepancy

pith-pipeline@v0.9.0 · 5552 in / 1417 out tokens · 52658 ms · 2026-05-12T03:12:05.078398+00:00 · methodology

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