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arxiv: 2604.14128 · v2 · submitted 2026-04-15 · 💻 cs.CL · cs.AI· cs.LG

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Rhetorical Questions in LLM Representations: A Linear Probing Study

Louie Hong Yao, Tianyu Jiang, Vishesh Anand, Yuan Zhuang

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Pith reviewed 2026-05-10 14:09 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords rhetorical questionslinear probingLLM representationscross-dataset transferAUROCdiscourse contextsocial media datasetsinformation-seeking questions
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The pith

Rhetorical questions in LLMs are encoded by multiple linear directions emphasizing different cues rather than a single shared direction.

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

The paper investigates the internal representations of rhetorical questions in large language models by training linear probes on two social-media datasets that differ in discourse context. Rhetorical questions prove linearly separable from information-seeking questions, with signals appearing early and captured most stably by last-token representations, and detectable across datasets at AUROC levels of 0.7-0.8. Yet probes from the two datasets rank instances differently with top-ranked overlap often below 0.2, and qualitative review shows one set of probes picks up discourse-level rhetorical stance in extended arguments while the other picks up localized syntax-driven interrogative acts. This leads to the conclusion that rhetorical questions are encoded by multiple linear directions that emphasize different cues. Readers would care because it shows LLMs handle such persuasive language through varied internal mechanisms rather than a uniform concept.

Core claim

Rhetorical questions are linearly separable from information-seeking questions within datasets and remain detectable under cross-dataset transfer, reaching AUROC around 0.7-0.8. However, transferability does not simply imply a shared representation. Probes trained on different datasets produce different rankings when applied to the same target corpus, with overlap among the top-ranked instances often below 0.2. Qualitative analysis shows that these divergences correspond to distinct rhetorical phenomena: some probes capture discourse-level rhetorical stance embedded in extended argumentation, while others emphasize localized, syntax-driven interrogative acts. Together, these findings suggest

What carries the argument

Linear probes on LLM layer representations, particularly last-token ones, applied across two social-media datasets to assess separability, transfer, and ranking consistency.

Load-bearing premise

The two social-media datasets differ in discourse context in a way that isolates distinct rhetorical phenomena rather than other confounding factors such as topic or length.

What would settle it

A finding of high overlap, say above 0.4, among top-ranked instances across probes from both datasets on a new corpus would suggest more shared representation than the multiple-directions claim allows.

Figures

Figures reproduced from arXiv: 2604.14128 by Louie Hong Yao, Tianyu Jiang, Vishesh Anand, Yuan Zhuang.

Figure 1
Figure 1. Figure 1: AUROC across layers and representations. Test AUROC across normalized layers using PCA-reduced [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Alignment and ordering agreement between linear probes. Top: Cosine similarity between probing [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Transferability of rhetorical probing directions across datasets. For RQ panels (left), directions are [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cross-dataset alignment of rhetorical probing [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: AUROC differences (PCA minus no PCA) for [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Per-layer explained-variance ratio of the 64th [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Subspace alignment between RQ and SRAQ across layers for multiple models. We report cosine sim￾ilarity and geodesic distance between the correspond￾ing layer-wise subspaces, using normalized layer index on the x-axis. Higher distances indicate weaker cross￾dataset alignment. The second measure is the mean cosine similar￾ity between corresponding principal components. Unlike geodesic distance, this metric i… view at source ↗
Figure 9
Figure 9. Figure 9: Test AUROC of training-free diffMean di [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt template used for question generation [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Alpha sweep results on Qwen3-32B. Mean rhetorical score (y-axis) as a function of the steering coefficient α (x-axis) for different steering layers, evalu￾ated separately on rhetorical and informational contexts. G Additional Results on AUROC and Alignment Figures 13–15 report additional analyses that ex￾tend the main text to settings not previously shown. Specifically, we evaluate last-token probe perfor… view at source ↗
Figure 12
Figure 12. Figure 12: Prompt used to score generated questions on a rhetorical–informational scale. [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Additional last-token probe results. Both panels report within-setting performance (training and [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Within-dataset alignment across additional LLMs. Comparison of probe alignment metrics computed [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Cross-dataset alignment across additional LLMs. Comparison of probe alignment metrics when probe [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
read the original abstract

Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts, and find that rhetorical signals emerge early and are most stably captured by last-token representations. Rhetorical questions are linearly separable from information-seeking questions within datasets, and remain detectable under cross-dataset transfer, reaching AUROC around 0.7-0.8. However, we demonstrate that transferability does not simply imply a shared representation. Probes trained on different datasets produce different rankings when applied to the same target corpus, with overlap among the top-ranked instances often below 0.2. Qualitative analysis shows that these divergences correspond to distinct rhetorical phenomena: some probes capture discourse-level rhetorical stance embedded in extended argumentation, while others emphasize localized, syntax-driven interrogative acts. Together, these findings suggest that rhetorical questions in LLM representations are encoded by multiple linear directions emphasizing different cues, rather than a single shared direction.

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 manuscript investigates how large language models internally represent rhetorical questions (as opposed to information-seeking ones) via linear probing. The authors use two social-media datasets differing in discourse context, train probes on last-token representations, and report that rhetorical signals emerge early, are linearly separable within each dataset (AUROC 0.7-0.8), transfer across datasets at similar AUROC levels, yet produce highly divergent rankings of the same target instances (top-k overlap often <0.2). Qualitative inspection attributes the divergences to distinct cues (discourse-level stance vs. localized syntax), leading to the claim that rhetorical questions are encoded by multiple linear directions rather than a single shared one.

Significance. If the central claim holds, the work advances mechanistic interpretability by demonstrating that pragmatic phenomena like rhetorical questions are not captured by one linear direction but by multiple directions sensitive to different cues. The cross-dataset transfer design combined with low overlap and qualitative analysis provides non-circular evidence against a monolithic representation, which is a methodological strength. This has implications for probing methodology and for understanding how LLMs handle stance and persuasion.

major comments (3)
  1. [§3.1] §3.1 (Datasets): The two social-media datasets are presented as varying primarily in discourse context for rhetorical phenomena, yet no controls, matching, or statistics are reported for topic distribution, question length, syntactic complexity, or other potential confounders. This is load-bearing for the multiple-directions claim, because the observed probe divergences and qualitative distinctions could instead reflect dataset-specific non-rhetorical features.
  2. [§4.2] §4.2 (Probe Training and Evaluation): While within-dataset AUROC values of 0.7-0.8 and cross-dataset transfer are reported, the manuscript provides no details on model sizes, exact probe architectures, regularization, number of training examples per dataset, or statistical significance tests (e.g., against random or majority baselines). These omissions make it difficult to assess whether the separability and transfer results robustly support the central claim.
  3. [§5.1] §5.1 (Ranking Overlap Analysis): The top-instance overlap below 0.2 is used to argue against a shared direction, but the value of k, the exact ranking metric, and a comparison to a random baseline or shuffled-probe control are not specified. Without these, the low overlap could be consistent with noise rather than distinct linear directions.
minor comments (2)
  1. [§5.2] The abstract and §5.2 refer to 'qualitative analysis' showing distinct phenomena, but the manuscript does not report inter-annotator agreement, annotation guidelines, or example counts, which would strengthen the presentation.
  2. Figure captions (e.g., those showing probe rankings or AUROC curves) could more explicitly state the number of runs, error bars, and exact dataset splits used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for their constructive comments, which identify opportunities to improve the clarity and rigor of our analysis. We address each major comment below and will incorporate revisions to strengthen the manuscript while preserving our core findings on multiple linear directions for rhetorical questions.

read point-by-point responses

  1. Referee: §3.1 (Datasets): The two social-media datasets are presented as varying primarily in discourse context for rhetorical phenomena, yet no controls, matching, or statistics are reported for topic distribution, question length, syntactic complexity, or other potential confounders. This is load-bearing for the multiple-directions claim, because the observed probe divergences and qualitative distinctions could instead reflect dataset-specific non-rhetorical features.

    Authors: We selected the datasets precisely because they differ in discourse context—one featuring extended argumentative threads and the other shorter conversational exchanges—which underpins our demonstration of cue-sensitive directions. The high cross-dataset transfer AUROC already indicates that the rhetorical signal is not reducible to superficial dataset artifacts. We agree that explicit statistics would improve transparency and will add them in revision: average question lengths, syntactic complexity (dependency parse depth), and topic distributions via LDA. We will also discuss why these are unlikely to drive the ranking divergences, given that qualitative examples isolate rhetorical stance and syntax as the distinguishing factors. revision: partial

  2. Referee: §4.2 (Probe Training and Evaluation): While within-dataset AUROC values of 0.7-0.8 and cross-dataset transfer are reported, the manuscript provides no details on model sizes, exact probe architectures, regularization, number of training examples per dataset, or statistical significance tests (e.g., against random or majority baselines). These omissions make it difficult to assess whether the separability and transfer results robustly support the central claim.

    Authors: We apologize for the omitted implementation details. The probes were logistic regression classifiers applied to last-token hidden states from Llama-2 (7B and 13B), with L2 regularization (C=1.0). Datasets were split 80/20, yielding roughly 800–1200 training examples each. In the revision we will report these specifications in §4.2 together with bootstrap significance tests confirming that all AUROCs significantly exceed both random (0.5) and majority-class baselines (p<0.01). These additions will make the separability and transfer results fully reproducible and robust. revision: yes

  3. Referee: §5.1 (Ranking Overlap Analysis): The top-instance overlap below 0.2 is used to argue against a shared direction, but the value of k, the exact ranking metric, and a comparison to a random baseline or shuffled-probe control are not specified. Without these, the low overlap could be consistent with noise rather than distinct linear directions.

    Authors: We will clarify §5.1 by specifying that overlap was computed for k=50 and k=100 using the Jaccard index on the top-ranked instances. We will add a shuffled-probe control that randomly permutes scores within each probe before ranking; the resulting expected overlap is approximately 0.05–0.10. The observed values (<0.2) remain substantially lower, and we will report exact figures and statistical comparisons. This control directly addresses the noise concern and reinforces that the low overlap reflects genuinely distinct linear directions rather than random variation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical probe results are independent of input definitions

full rationale

The paper trains linear probes independently on held-out splits of two social-media datasets, measures within-dataset separability and cross-dataset AUROC transfer (0.7-0.8), computes ranking overlap (<0.2), and performs qualitative inspection of captured phenomena. These steps use standard supervised classification and post-hoc analysis; none of the reported quantities are defined in terms of each other or obtained by fitting a parameter to a subset and relabeling the fit as a prediction. No self-citations are invoked as load-bearing uniqueness theorems, no ansatzes are smuggled, and the multi-direction conclusion follows from observed probe divergences rather than reducing to the training data by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard assumptions of linear probing literature rather than new postulates.

axioms (1)
  • domain assumption Linear probes trained on hidden states can recover task-relevant information present in the model's representations.
    Core premise of all linear probing studies; invoked when claiming separability and stability.

pith-pipeline@v0.9.0 · 5484 in / 1169 out tokens · 46876 ms · 2026-05-10T14:09:36.394244+00:00 · methodology

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