Quantifier Scope Interpretation in Language Learners and LLMs

Shaohua Fang; Yan Cong; Yue Li

REVIEW 3 major objections 6 minor 45 references

Quantifier Scope Interpretation in Language Learners and LLMs

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Most LLMs prefer surface-scope readings, like humans, but only a subset reproduce the English–Chinese contrast in inverse-scope availability.

desk verdict Useful cross-linguistic LLM scope benchmark, but the abstract's surface-scope preference claim is contradicted by the paper's own EU results. read the letter →

arxiv 2509.10860 v1 pith:7IYKDF2P submitted 2025-09-13 cs.CL

Shaohua Fang , Yue Li , Yan Cong This is my paper · ORCID

classification cs.CL

keywords quantifierscopelargelanguagemodelscross-linguisticvariationhumansimilarityscoresurprisaltruth-valuejudgmenttaskEnglishandChinesesemantictransparency

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

This paper asks whether large language models interpret doubly quantified sentences the way humans do, in both English and Chinese. Its central claim is that most LLMs prefer the surface-scope reading, the interpretation humans also favor, while only a subset of models reproduce the human-like contrast where inverse scope is more available in English than in Chinese. The authors read this as partial evidence that LLMs can approximate human interpretive preferences, with model architecture, scale, and especially the language of pre-training data affecting how close the approximation is. The reason to care is that quantifier scope is a case where a sentence's meaning depends on syntax, semantics, and context, so it can reveal whether a model represents meaning rather than only surface word statistics.

What carries the argument

The load-bearing machinery is surprisal-based interpretation scoring: each model assigns a conditional probability to a target sentence given a story context, and the reading whose context yields lower surprisal is treated as preferred. Masked models are scored by pseudo-log-likelihood and autoregressive models by chain-rule token probabilities. The second component is the Human Similarity score, a Jensen-Shannon divergence between an LLM's response distribution and human per-item acceptability ratings, with lower divergence meaning greater human-likeness. This turns the psycholinguistic truth-value judgment task into a probability comparison and makes cross-model and cross-language differences measurable.

What would settle it

Run the same seven LLMs on the exact item set given to human participants and compare surprisal-ranked preferences with human ratings item by item. If the models no longer show inverse scope more likely in English than in Chinese, or if a direct 7-point acceptability probe of Chinese inverse-scope items returns ratings equal to surface-scope ratings, the claim that a subset of LLMs captures human-like cross-linguistic scope contrast fails.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that seven LLMs, scored by the conditional probability that a sentence is accepted in a context favoring one reading, mostly prefer surface scope for universal-existential sentences such as “Every child climbed a tree,” matching humans. For the reverse construction “A child climbed every tree,” most LLMs prefer the inverse reading, which is the opposite of the human pattern. Only the two BERT models show inverse scope being more likely in English than in Chinese for both constructions, mirroring the human cross-linguistic contrast. Human Similarity scores, computed with Jensen-Shannon divergence, show the GPT-family models closest to human judgments, LLaMA intermediate, and BERT lowest, especially against Chinese speakers; training-language effects appear but are not consistent, with the Chinese-trained GPT-2 failing to replicate the Chinese-trained LLaMA's behavior.

Load-bearing premise

The load-bearing premise is that surprisal scores computed on the expanded story contexts measure the same scope-interpretation preference as the human truth-value judgments on the original, shorter stories, and the paper itself flags that the items were not identical.

Editorial extensions

If this is right

Quantifier scope becomes a reusable diagnostic: a model's preference between surface and inverse scope can be read off surprisal without prompting.
If this result generalizes, autoregressive models (GPT, LLaMA) are better global proxies for human judgments, while masked models (BERT) are better at capturing one specific cross-linguistic contrast.
The language of pre-training data does not guarantee human-like cross-linguistic behavior: the Chinese-trained GPT-2 did not reproduce the Chinese-trained LLaMA's pattern.
The EU result, where most LLMs prefer inverse scope while humans prefer surface scope, marks a boundary case for using LLMs as models of human sentence processing.
Because BERT models were the only ones showing inverse scope more likely in English than in Chinese, architecture likely modulates how language-specific semantic knowledge is encoded.

Reading between the lines

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

If the LLM items were made identical to the human items, the reported Human Similarity ordering could change; the paper's own limitation note says the LLM items were expanded, so the ranking is not final.
The universal LLM preference for inverse scope in EU sentences could mean LLMs exploit the entailment from 'a child climbed every tree' differently than humans do; a test using contexts that block that entailment would show whether the preference disappears.
A direct 7-point acceptability probe of inverse-scope Chinese items, along the lines of the exploratory Deepseek test, would settle whether the human-like English–Chinese difference is a grammatical contrast or only a relative surprisal effect.
The 'LLMs behave like L2 learners' pattern suggests cross-linguistic transfer in training data; training the same architecture on carefully balanced English and Chinese corpora could test whether the English-like bias disappears.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's what to know: this is a legitimate extension of LLM scope probing to Chinese and to EU structures, but the abstract and conclusion say 'most LLMs prefer SS' when the EU results in Table 3 show the opposite. That mismatch is the first thing to fix.

What's new: Kamath et al. (2024) only tested English 'every...a' sentences. This paper adds existential-first (EU) constructions, Chinese materials, L2 human comparison groups, seven models, and a Deepseek-R1 exploratory probe. That is a real step forward, and the surprisal-based method is sound. The BERT-vs-GPT/LLaMA contrast—BERT tracks English/Chinese IS availability, the autoregressive models do not—is the most interesting finding. The HS scores add a descriptive layer, though they're only as good as the comparison.

The load-bearing soft spot: the headline claim is only true for UE. For EU, no model significantly prefers SS; every significant contrast favors IS (DistilGPT2, GPT-2En, LlamaEn, LlamaCh in English; BERT-large, LlamaEn in Chinese). The Discussion calls EU 'unexpected' and admits humans favored SS for both structures. So you can't say 'most LLMs prefer the surface scope interpretations, aligning with human tendencies' without restricting it to UE. This is not a minor wording issue; it changes the takeaway. The EU inverse-scope preference should be reported as a central negative result.

Experiment 2 is weaker. The LLM items are enriched expansions of the human items, not identical. The authors flag this in Limitations but then say the HS metric does not strictly require identical items. That's defensible for a descriptive metric, but it undercuts any specific claim about how closely LLMs match human judgments. There are also no error bars or baselines on the HS scores, and the p=.0499 Language effect for BERT-large on UE is thin. The human data come from the first author's thesis; that's not a problem by itself, but it's a single source.

The citation pattern is fine, and there's no circularity—surprisals are computed directly from pretrained weights. This paper is for psycholinguists and LLM interpretability researchers. It deserves a serious referee. My recommendation: send to peer review, but require the authors to restrict every SS-preference claim to UE, report the EU result prominently, and either share code/data or explain why not. With those revisions, it's a solid empirical contribution.

Referee Report

3 major / 6 minor

Summary. The paper studies how seven LLMs interpret doubly quantified sentences in English and Chinese, using surprisal-based probabilities to infer preference for surface scope (SS) versus inverse scope (IS) in two structures: UE (e.g., "Every child climbed a tree") and EU (e.g., "A child climbed every tree"). Experiment 1 reports that for UE, most models prefer SS over IS, with some language-dependent variation; for EU, models predominantly prefer IS, a pattern the authors call unexpected. Experiment 2 compares LLM surprisal distributions with human truth-value judgment data from Fang (2023) using a Jensen-Shannon-divergence-based Human Similarity score, finding that GPT and LLaMA models are more human-like than BERT models and that L2-learner-like patterns emerge. The abstract and conclusion claim that most LLMs prefer surface scope and align with human tendencies, and that only some models differentiate English from Chinese in IS availability.

Significance. If the surface-scope claim were restricted to UE constructions, the study would contribute a useful cross-linguistic, cross-architecture dataset for LLM quantifier-scope behavior, and the direct computation of surprisals from pretrained weights without any fitting to the target results is a methodological strength. The inclusion of both UE and EU constructions and Chinese materials goes beyond prior LLM scope work, and the EU inverse-scope preference is a falsifiable negative result. However, the overgeneralized headline claim and the non-identical stimuli in Experiment 2 mean that the human-alignment conclusions, as currently stated, are not supported by the evidence presented.

major comments (3)

[Abstract, §5 Conclusion, §4.1 Discussion] The claim that "most LLMs prefer the surface scope interpretations" is contradicted by the paper's own Experiment 1 results for EU sentences. Section 2.4 states that for EU structures "all LLMs predominantly preferred IS readings," and Table 3 shows that every statistically significant SS-vs-IS contrast favors inverse scope (e.g., DistilGPT2, GPT-2En, LlamaEn, and LlamaCh in English; BERT-large and LlamaEn in Chinese), with no EU cell showing a significant surface-scope preference. The authors themselves describe the EU results as "unexpected" (§4.1). Since EU constitutes half the materials, all occurrences of the surface-scope preference claim must be restricted to UE constructions, and the EU inverse-scope preference should be reported as a central negative result rather than as an aside.
[§3.2, §3.3, §6 Limitations] The Human Similarity comparison in Experiment 2 relies on non-identical stimuli for humans and LLMs: human ratings came from the original story contexts in Fang (2023), while LLM surprisals were computed on expanded and enriched contexts (Section 2.1). As the authors acknowledge in Section 6, the items "were not identical," and this discrepancy threatens the construct validity of HS scores, because surprisal differences may reflect context length or detail rather than scope interpretation. The HS analysis also lacks a baseline (e.g., human-human agreement or a chance-level reference) and Figure 4 shows no error bars or per-item dispersion. Without identical items, a baseline, and uncertainty estimates, the claim that HS scores show LLMs "approximate human language use" is not fully supported; the authors should either rerun the comparison on identical items or substantially temper these alignment claims.
[§2.4 Results] The only significant Language effect in the per-LLM analyses is BERT-large in the UE condition, reported as b=1.1, p=.0499. Because seven models and two structures are tested, this borderline p-value, uncorrected for multiple comparisons, is too weak to support the Discussion's emphasis on BERT models exhibiting cross-linguistic contrasts. The authors should report multiplicity-adjusted p-values or, at minimum, all model-specific p-values, and in the Discussion distinguish strong effects (e.g., the EU patterns for LlamaCh) from this borderline finding.

minor comments (6)

[§3.3] There is a typo: "ANOV A tests" should read "ANOVA tests."
[Figure 3 caption] "Experimental 2" should be "Experiment 2."
[§2.4] The inline text contains "allps" without a space; it should read "all ps."
[§4.2] The sentence "As shown in Tables 1 and 2" appears to refer to the results tables; it should refer to Tables 2 and 3, since Table 1 is a gloss of the Chinese sentence.
[§2.1] The labels UE and EU are not defined explicitly; "30 existential quantifier (UE)" is confusing because UE denotes the universal-existential structure "Every ... a ..." and EU denotes the existential-universal structure "A ... every ...". Please define the labels at first use.
[§4.3] The exploratory Deepseek-R1 analysis lacks details about model version, sampling parameters, and prompt robustness; since it is not central to the paper's claims, it would fit better in a clearly marked exploratory subsection or in the appendix.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: LLM surprisals are direct outputs, the human baseline is independent, and the flagged issues are validity/consistency caveats rather than circular reductions.

full rationale

Adjudication: no circular step. Experiment 1 computes surprisals directly from pretrained weights using Minicons and compares SS vs IS surprisal; no parameter is fitted to the target preference and no model is trained on the scope task. The binary SS/IS labels are simply lower-surprisal comparisons, not predictions derived from fitted inputs. Experiment 2's human baseline comes from Fang (2023), a self-citation by the first author, but it supplies independent empirical judgments; the HS metric (Duan et al., 2024) is a post-hoc descriptive comparison and does not feed back into the LLM surprisals. The load-bearing caveat is validity, not circularity: Section 6 states, 'the items used for deriving LLM data were not identical to those used in the human experiment... this discrepancy may weaken the validity of direct comparisons,' which should temper the human-alignment claims but does not make them circular. Separately, the abstract's unqualified 'most LLMs prefer the surface scope' conflicts with Section 2.4's 'For EU structures, all LLMs predominantly preferred IS readings' and Table 3, but that is an internal consistency/overgeneralization problem, not a derivation-from-inputs problem. Score 0.

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

No new entities or fitted theory parameters are introduced; the central measurements are surprisals from pretrained models compared against human ratings. The only fitted quantities are statistical model coefficients, which are not free parameters of a scientific claim.

assumptions (5)

domain assumption Surprisal (lower value) indicates preferred interpretation for an LLM.
Section 2.2 assigns binary labels and compares surprisals to infer SS vs IS preference; this mapping is assumed but not validated against LLM introspection or prompting.
domain assumption Pseudo-log-likelihood scoring for masked models (BERT) yields a valid sentence probability proxy.
Section 2.2 aggregates per-token masked prediction probabilities into surprisals for BERT; this is a known approximation, not a true joint probability.
domain assumption LLM items expanded from human items preserve the same scope biasing as the original human TVJT stories.
Section 3.1 and Limitations state LLM items were enriched expansions of Fang (2023) items; the HS comparison assumes the two item sets measure the same construct.
domain assumption Chinese only permits surface scope in the relevant configurations (scope rigidity).
Adopted in Section 1 as the prevailing theoretical view (Aoun and Li 1989); used to motivate cross-linguistic predictions. The paper later notes empirical nuance (Fang 2023; Scontras et al. 2017).
domain assumption Human Similarity score based on Jensen-Shannon divergence validly indexes human-LLM alignment.
Section 3.2 adopts Duan et al. (2024) HS; the paper treats it as descriptive and does not validate it against a null distribution or human-human ceiling.

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Cite this review

Pith. "Pith review of Quantifier Scope Interpretation in Language Learners and LLMs." pith.science (2026). https://pith.science/paper/7IYKDF2P

@misc{pith2026250910860,
  author       = {Pith},
  title        = {Pith review of: Quantifier Scope Interpretation in Language Learners and LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7IYKDF2P}},
  note         = {Machine review of arXiv:2509.10860}
}

read the original abstract

Sentences with multiple quantifiers often lead to interpretive ambiguities, which can vary across languages. This study adopts a cross-linguistic approach to examine how large language models (LLMs) handle quantifier scope interpretation in English and Chinese, using probabilities to assess interpretive likelihood. Human similarity (HS) scores were used to quantify the extent to which LLMs emulate human performance across language groups. Results reveal that most LLMs prefer the surface scope interpretations, aligning with human tendencies, while only some differentiate between English and Chinese in the inverse scope preferences, reflecting human-similar patterns. HS scores highlight variability in LLMs' approximation of human behavior, but their overall potential to align with humans is notable. Differences in model architecture, scale, and particularly models' pre-training data language background, significantly influence how closely LLMs approximate human quantifier scope interpretations.

Figures

Figures reproduced from arXiv: 2509.10860 by the authors.

**Figure 2.** LLM surprisal distributions for surface vs. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Overview of Experimental 2 design, illustrat [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Review history (2 revisions) →

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online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...
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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Catherine Anderson. 2004. The structure and real-time comprehension of quantifier scope ambiguity. Northwestern University

work page 2004

[2] [2]

Joseph Aoun and Yen-hui Audrey Li. 1989. Scope and constituency. Linguistic inquiry, 20(2):141--172

work page 1989

[3] [3]

Adrian Brasoveanu and Jakub Dotla c il. 2015. Strategies for scope taking. Natural Language Semantics, 23:1--19

work page 2015

[4] [4]

James Britton, Yan Cong, Yu-Yin Hsu, Emmanuele Chersoni, and Philippe Blache. 2024. On the influence of discourse connectives on the predictions of humans and language models. Frontiers in Human Neuroscience, 18:1363120

work page 2024

[5] [5]

Zhenguang G Cai, Xufeng Duan, David A Haslett, Shuqi Wang, and Martin J Pickering. 2024. Do large language models resemble humans in language use? arXiv preprint arXiv:2303.08014

arXiv 2024

[6] [6]

Chia-Ying Chu, Alison Gabriele, and Utako Minai. 2014. Acquisition of quantifier scope interpretation by chinese-speaking learners of english. In Selected proceedings of the 5th Conference on Generative Approaches to Language Acquisition North America, pages 157--168

work page 2014

[7] [7]

Yan Cong, Emmanuele Chersoni, Yu-Yin Hsu, Alessandro Lenci, and 1 others. 2023. Are language models sensitive to semantic attraction? a study on surprisal. Association for Computational Linguistics

work page 2023

[8] [8]

Yiming Cui, Ziqing Yang, and Xin Yao. 2023. Efficient and effective text encoding for chinese llama and alpaca. arXiv preprint arXiv:2304.08177

arXiv 2023