Examining the Robustness of Large Language Models across Language Complexity

Jiayi Zhang

REVIEW 3 major objections 7 minor 10 references

Examining the Robustness of Large Language Models across Language Complexity

T0 review · 3 major / 7 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read LLM-based detectors of self-regulated learning are not uniformly robust: they handle lexical complexity well, but their accuracy shifts with syntactic and semantic complexity.

desk verdict A clear descriptive audit of LLM-based SRL detectors across three complexity measures, but the headline robustness differences in Table 3 need confidence intervals or significance tests before being taken as evidence. read the letter →

arxiv 2501.18738 v1 pith:PR75O4I2 submitted 2025-01-30 cs.CL

Jiayi Zhang This is my paper · ORCID

classification cs.CL

keywords largelanguagemodelsmodelrobustnesscomplexityself-regulatedlearningtextembeddingsAUCeducationaldataminingfairness

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 LLM-based detectors of self-regulated learning work equally well on student text of different complexity, and reports that they do not always. Across four detectors trained on OpenAI text embeddings, performance was comparable for text with high versus low lexical complexity. For syntactic complexity, the contextual-representation detector performed better on more complex syntax, while the data-transformation detector performed better on simpler syntax. For semantic cohesion, the numerical-representation detector performed better on more cohesive text and the contextual-representation detector on less cohesive text. The concern is fairness: if detectors are less accurate for some writing styles, students with those styles may be misclassified.

What carries the argument

The machinery is an LLM-embedding-plus-classifier pipeline audited against three linguistic complexity measures. Each Thinklet (a four-phase math problem-solving activity in the CueThink platform) has its concatenated student responses vectorized with a pretrained sentence-embedding model into 1536-dimensional embeddings, and a one-hidden-layer neural network predicts the presence of each SRL construct from those embeddings. Complexity is quantified by the MASS lexical richness score, Coh-Metrix syntactic simplicity, and Coh-Metrix deep cohesion; a median split on each measure divides Thinklets into high and low complexity groups, and detector quality is compared via AUC. The load-bearing step is that the same trained detector is evaluated separately on each group, so any AUC gap is attributed to the text property rather than to model differences.

What would settle it

A concrete check is to replicate the study on a larger corpus with a wider range of writing styles, computing AUC by complexity group with bootstrapped confidence intervals; if the reported gaps (for example, NR 0.845 versus 0.955 by deep cohesion) shrink to within chance, the claim of complexity-dependent robustness is not supported.

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Extended reading notes

Core claim

The paper's central finding is that robustness to language complexity is detector- and dimension-specific. All four SRL detectors—numerical representation, contextual representation, outcome orientation, and data transformation—showed comparable AUCs for low- versus high-lexical-complexity text, consistent with the narrow observed range of MASS scores. On syntactic complexity, the contextual representation model had higher AUC for more syntactically complex text (0.851 vs 0.739), while the data transformation model had higher AUC for less syntactically complex text (0.815 vs 0.683). On semantic complexity, the numerical representation model had higher AUC for high-cohesion text (0.955 vs 0.845), while the contextual representation model had higher AUC for low-cohesion text (0.813 vs 0.652). The paper interprets these patterns through how each SRL construct is typically expressed in student writing.

Load-bearing premise

The load-bearing premise is that the three linguistic complexity scores for these short student responses are accurate enough that splitting them at the median really separates 'complex' from 'simple' text; if the scores are noisy or the split is arbitrary, the reported performance gaps could be artifacts.

Editorial extensions

If this is right

If the finding holds, robustness audits that report overall AUC alone are insufficient; detectors should report performance by complexity subgroups to detect differential accuracy.
The contextual-representation and data-transformation detectors should be treated as less reliable for one end of the syntactic-complexity distribution, which may misclassify students who write in those styles.
Numerical-representation and contextual-representation detectors need checking for cohesion: high-cohesion text favors NR, low-cohesion text favors CR.
Lexical complexity appears to be a minor risk for this text type, so future audits can focus on syntax and cohesion.
Replication on datasets with wider lexical variability is needed to know whether the lexical-complexity null result generalizes.

Reading between the lines

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

The reported gaps could reflect construct-style confounds rather than complexity per se: if students who produce numerical representations tend to write more cohesive text, the NR detector's advantage on high-cohesion text may be driven by the construct's typical surface form, not by the detector's sensitivity to cohesion.
A natural extension is to model complexity continuously and test for interactions, rather than relying on a median split, which discards within-group variation and can inflate or hide threshold effects.
The same audit design could be applied to other LLM-based classifiers in education, such as peer-feedback quality, where fairness across writing styles is a concern.
If the patterns generalize, a testable remedy is to augment training data by paraphrasing student responses across complexity levels or to use complexity as a covariate during evaluation.

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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

You should know this is a small empirical paper, not a methodological advance. It takes four existing SRL detectors, splits 182 Thinklets into high/low groups on three linguistic measures, and reports AUCs per group. The descriptive patterns are plausible, and the lexical-complexity result (models comparable because the text is homogeneous) is sensible. What's new is the specific subgroup AUC comparisons, which I don't think are in the cited literature.

Credit where due: the overall model evaluation uses student-level 10-fold CV with means and SDs; the paper is clearly written; the choice of MASS, syntactic simplicity, and deep cohesion is reasonable for this text type; and the discussion about why syntactic and semantic complexity might affect CR versus DT is thoughtful, even if speculative. The authors also correctly exclude strategy orientation due to rarity instead of forcing a model on 14 labels.

The soft spot is the central robustness claim. Section 3.2 and Table 3 compare point estimates of AUC across median-split groups with no confidence intervals, standard errors, or significance tests. With about 91 Thinklets per group, drawn from only 79 students, and multiple Thinklets per student, the observed differences (CR by syntactic simplicity .851 vs .739, NR by deep cohesion .845 vs .955, CR by deep cohesion .813 vs .652) are well within plausible sampling variability. The text also says the threshold for a 'noticeable' difference is 'greater than one' in AUC, which is impossible; presumably they mean 0.1, but the threshold is arbitrary and unexplained. These issues are fixable: per-fold group AUCs, bootstrap CIs, or a paired test would settle whether the effects are systematic. The paper also doesn't release code or data, so the analysis is not independently checkable.

A minor point: the paper describes 'syntactic simplicity' as inversely reflecting syntactic complexity, and the interpretation is correct, but the naming invites confusion in the tables and text. Nothing here is a fatal flaw; the paper is honest and the question is real.

Who this is for: researchers in educational data mining and learning analytics who build or audit LLM-based detectors, and anyone concerned about fairness of automated assessment across language proficiency. It deserves a serious referee: the question is relevant and the paper is a clear first step, but it needs revision to add uncertainty quantification, fix the threshold statement, and ideally share artifacts. I would send it to review, expecting major revision before it supports its conclusions.

Referee Report

3 major / 7 minor

Summary. The paper examines whether LLM-based detectors of self-regulated learning (SRL) in middle-school math problem-solving perform equally well on text with high versus low lexical, syntactic, and semantic complexity. Using 182 Thinklets from 79 students, the authors compute MASS, syntactic simplicity, and deep cohesion scores, median-split the data into high/low complexity groups, and compare area-under-the-curve (AUC) values of four detectors (numerical representation, contextual representation, outcome orientation, data transformation) across groups. They report that lexical complexity has little effect, while syntactic and semantic complexity are associated with detector-specific performance differences. The paper claims this is the first study of such robustness for LLM-based student models.

Significance. If the reported differences are real, the findings would be practically important for fairness and reliability of LLM-based educational detectors, an under-studied area. The paper uses externally defined linguistic measures rather than deriving complexity from the detectors themselves, so circularity is not a central concern. The authors also rely on student-level cross-validation (Section 3.1), which is appropriate for the correlated data structure. However, the central robustness claim—that detectors perform better for one complexity group than another—rests on point estimates in Table 3 with no confidence intervals, standard errors, or significance tests. The paper therefore provides a plausible and relevant research question but does not yet supply the statistical evidence needed to support its conclusions.

major comments (3)

[Section 3.2, Table 3] The central robustness comparisons are reported as point estimates only. The text highlights differences using an unexplained threshold, and the stated criterion 'greater than one' is impossible for an absolute difference in AUC values (which must lie between 0 and 1). For example, the CR detector's AUC differs by 0.112 for syntactic simplicity (0.851 vs. 0.739) and by 0.161 for deep cohesion (0.813 vs. 0.652); the NR detector differs by 0.110 for deep cohesion (0.845 vs. 0.955). With 182 Thinklets from 79 students, high/low groups of roughly 91 units, and clustered, unbalanced data, these differences could plausibly arise from sampling variability. The conclusions that a model 'performs better' for a given complexity group are not supported without per-fold group AUCs, bootstrap confidence intervals, or a paired significance test. Please provide uncertainty quantification for every comparison in Table 3, and correct the threshold description (presumably 'greater than 0.1').
[Section 2.3, Table 2] The validity of the linguistic complexity measures for this text type is load-bearing. The MASS score has a median of 0.03 and a maximum of 0.34 on a theoretical 0–1 range, suggesting very short or lexically homogeneous texts; the deep cohesion distribution is highly skewed (SD = 40 with median 70). The paper does not report text lengths, label distributions, or any evidence that these instruments behave as intended for short, mixed-purpose student responses within a Thinklet. If MASS and the Coh-Metrix indices do not validly separate high- and low-complexity text in this corpus, the group comparisons in Table 3 could be artifacts. Please report the distribution of text lengths, examine the range and reliability of each measure in this corpus, and either provide a validation against human judgments or clearly discuss this limitation as a threat to the robustness conclusions.
[Sections 2.2.2 and 3.2] The paper does not specify how the group-specific AUCs in Table 3 are computed relative to the 10-fold student-level cross-validation used in Section 3.1. For example, are the AUCs pooled across test folds, or are they averaged from per-fold estimates? This distinction matters because rare constructs (SO was excluded with only 14 labels; OO and DT are described as rarer) may yield unstable fold-level AUCs when further split by complexity group. Please clarify the exact computation and report the number of positive labels per construct and per complexity group, along with the per-fold variability, so that the reader can assess the reliability of each reported AUC.

minor comments (7)

[Section 2.1] The sentence 'As 1 shows' should read 'As Figure 1 shows'.
[Section 2.2.1] The inter-rater reliability section would benefit from reporting the number of Thinklets used for double-coding and clarifying that the kappa values are Cohen's kappa.
[Section 2.2.2] The description of the embedding model as 'based on each word in a sentence and the words surrounding it' is imprecise for a sentence-embedding model; please clarify the architecture and cite the correct reference for OpenAI's embedding-3-short model.
[Table 2] The 'Range' column for MASS reports '0-1,' but the observed minimum and maximum are 0 and 0.34; please clarify whether 'Range' refers to the theoretical range or the observed range.
[Section 3.1] The standard deviations in parentheses for average AUCs should be labeled (e.g., across the 10 folds) so that readers know what variability is being reported.
[References] The Dentella, Murphy, Marcus, and Leivada (2023) reference is incomplete; it lacks publication venue, volume, or DOI information.
[Section 4] The explanations for the observed differences (e.g., why data transformation text has simpler syntax) are speculative; please label them as hypotheses rather than conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the robustness analysis compares model AUCs against external linguistic complexity measures; no fitted parameter is renamed as a prediction.

full rationale

The paper's central comparison uses three external linguistic measures (MASS, Coh-Metrix syntactic simplicity, deep cohesion) to median-split Thinklets, and then reports AUCs of SRL detectors on each group. The SRL labels come from human coding, and the detectors are trained on LLM embeddings; the complexity measures are computed independently of the detector outputs. No equation in the paper defines a linguistic measure in terms of model performance, and no model performance value is derived from a complexity measure. The self-citations (Zhang et al., 2022 for the platform; Zhang et al., 2024 for LLM-based SRL detection) are references to prior work and are not used to justify the robustness finding; the actual AUC table is computed from the present data and external measures. The lack of confidence intervals or significance tests for the AUC differences in Table 3 is a statistical-evidence concern, not a circularity concern, and the 'greater than one' threshold appears to be a typo for a delta threshold. Thus there is no specific reduction to the paper's inputs that meets the bar for a circularity finding.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No free parameters are fit to derive a result, but the hand-chosen difference threshold and the reliance on standard linguistic measures are the main input choices. No new entities are postulated. The detectors themselves have trained weights from prior work, but the paper does not report them.

free parameters (1)

AUC difference threshold for 'noticeable' performance gap = 0.1 (paper states 'greater than one', likely a typo)
Table 3 highlights comparisons where the absolute AUC difference exceeds a hand-set threshold. The stated 'greater than one' is impossible for AUCs, so the actual criterion is ambiguous; this threshold is not derived or justified.

assumptions (3)

domain assumption The embeddings produced by the OpenAI model ('sentence-embedding-3-short') are a faithful representation of student text for SRL detection.
Section 2.2.2 uses these embeddings as the only text representation. If the embedding model is misidentified or unsuitable, all detector performance and subgroup comparisons are affected.
domain assumption MASS, Coh-Metrix syntactic simplicity, and deep cohesion validly measure lexical, syntactic, and semantic complexity on short Thinklet responses.
Section 2.3 relies on these measures to assign Thinklets to high/low complexity groups without validating them on this text type.
domain assumption The human-coded SRL labels used as ground truth are sufficiently reliable.
Section 2.2.1 reports inter-rater kappas of 0.63 to 0.83. The CR kappa of 0.63 is moderate, but the labels are used to train and evaluate all detectors.

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Cite this review

Pith. "Pith review of Examining the Robustness of Large Language Models across Language Complexity." pith.science (2026). https://pith.science/paper/PR75O4I2

@misc{pith2026250118738,
  author       = {Pith},
  title        = {Pith review of: Examining the Robustness of Large Language Models across Language Complexity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PR75O4I2}},
  note         = {Machine review of arXiv:2501.18738}
}

read the original abstract

With the advancement of large language models (LLMs), an increasing number of student models have leveraged LLMs to analyze textual artifacts generated by students to understand and evaluate their learning. These student models typically employ pre-trained LLMs to vectorize text inputs into embeddings and then use the embeddings to train models to detect the presence or absence of a construct of interest. However, how reliable and robust are these models at processing language with different levels of complexity? In the context of learning where students may have different language backgrounds with various levels of writing skills, it is critical to examine the robustness of such models to ensure that these models work equally well for text with varying levels of language complexity. Coincidentally, a few (but limited) research studies show that the use of language can indeed impact the performance of LLMs. As such, in the current study, we examined the robustness of several LLM-based student models that detect student self-regulated learning (SRL) in math problem-solving. Specifically, we compared how the performance of these models vary using texts with high and low lexical, syntactic, and semantic complexity measured by three linguistic measures.

Figures

Figures reproduced from arXiv: 2501.18738 by the authors.

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Reference graph

Works this paper leans on

10 extracted references · 7 canonical work pages

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Darvishi, A., Khosravi, H., Sadiq, S., & Ga ˇsevi´c, D. (2022). Incorporating ai and learning analytics to build trustworthy peer assessment systems. British Journal of Educational Technology, 53(4), 844–875. doi: 10.1111/bjet.13233

work page doi:10.1111/bjet.13233 2022
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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [1]

Darvishi, A., Khosravi, H., Sadiq, S., & Ga ˇsevi´c, D. (2022). Incorporating ai and learning analytics to build trustworthy peer assessment systems. British Journal of Educational Technology, 53(4), 844–875. doi: 10.1111/bjet.13233

work page doi:10.1111/bjet.13233 2022

[2] [2]

Dentella, V ., G¨unther, F., & Leivada, E. (2023). Systematic testing of three language models reveals low language accuracy, absence of response stability, and a yes-response bias. Proceedings of the National Academy of Sciences , 120(51), e2309583120. doi: 10.1073/ pnas.2309583120

work page 2023

[3] [3]

Dentella, V ., Murphy, E., Marcus, G., & Leivada, E. (2023). Testing ai performance on less frequent aspects of language reveals insensitivity to underlying meaning

work page 2023

[4] [4]

S., Louwerse, M

McNamara, D. S., Louwerse, M. M., McCarthy, P. M., & Graesser, A. C. (2010). Coh- metrix: Capturing linguistic features of cohesion. Discourse Processes, 47(4), 292–330. doi: 10.1080/01638530902959943

doi:10.1080/01638530902959943 2010

[5] [5]

M., Tworek, J.,

Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., . . . Weng, L. (2022). Text and code embeddings by contrastive pre-training. Retrieved from http://arxiv .org/abs/2201.10005

arXiv 2022

[6] [6]

Chen, G. (2021). Assessing algorithmic fairness in automatic classifiers of educa- 7 tional forum posts. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V . Dimitrova (Eds.), Artificial intelligence in education (Vol. 12748, pp. 381–394). Springer Interna- tional Publishing. doi: 10.1007/978-3-030-78292-4 31

doi:10.1007/978-3-030-78292-4 2021

[7] [7]

Torruella, J., & Capsada, R. (2013). Lexical statistics and tipological structures: A measure of lexical richness. Procedia - Social and Behavioral Sciences, 95, 447–454. doi: 10.1016/ j.sbspro.2013.10.668

work page 2013

[8] [8]

Winne, P. H. (2017). Learning analytics for self-regulated learning. In Handbook of learning analytics (pp. 531–566). Society for Learning Analytics Research (SoLAR)

work page 2017