arxiv: 2604.07006 · v1 · submitted 2026-04-08 · 💻 cs.CL

Continuous Interpretive Steering for Scalar Diversity

Ye-eun Cho This is my paper

Pith reviewed 2026-05-10 18:03 UTC · model grok-4.3

classification 💻 cs.CL

keywords scalar implicaturepragmatic inferenceactivation steeringscalar diversitylarge language modelsgraded interpretationinterpretability

0 comments

The pith

Graded activation steering recovers item-specific differences in scalar implicature strength in language models.

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

The paper introduces Continuous Interpretive Steering as a way to treat activation steering strength as a continuous variable rather than a fixed prompt adjustment, allowing tests of how language models handle varying degrees of pragmatic enrichment. It pairs this with a new GraSD dataset that assigns graded scalar diversity scores to different lexical items. Experiments across four models demonstrate that uniform steering raises pragmatic interpretations equally for all items and flattens natural differences, while varying the steering strength produces shifts that track the pre-assigned grades. This matters because it shows the gradation is not just a surface behavior but is structured inside the model's representations in a form that targeted interventions can access and modulate.

Core claim

The central claim is that graded sensitivity to scalar diversity is encoded in the representation space of LLMs and can be systematically recovered through controlled intervention: uniform activation steering increases pragmatic interpretations globally but collapses item-level variation, whereas graded activation steering yields differentiated interpretive shifts aligned with scalar diversity grades from the GraSD dataset.

What carries the argument

Continuous Interpretive Steering (CIS), which varies activation steering strength as a continuous experimental variable to induce and measure graded changes in pragmatic interpretation.

If this is right

Uniform activation steering increases pragmatic interpretations globally across scalar items but erases natural item-level differences in implicature strength.
Graded activation steering produces interpretive shifts that align with the scalar diversity grades encoded in the GraSD dataset.
Graded sensitivity to scalar implicatures is represented in the model's activation space in a form recoverable by continuous intervention.
CIS combined with GraSD supplies a framework for evaluating and manipulating graded pragmatic sensitivity beyond prompt-based methods.

Where Pith is reading between the lines

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

The same continuous steering approach could be tested on other graded pragmatic phenomena such as uncertainty expressions or politeness levels to check whether similar internal gradations exist.
If internal representations encode scalar diversity this way, targeted steering might offer a route to adjust model outputs for context-sensitive inference without retraining.
The method opens the possibility of using steering strength as a diagnostic tool to compare how different model architectures or training regimes preserve or lose graded pragmatic structure.

Load-bearing premise

Varying activation steering strength produces clean, isolated changes in pragmatic interpretation without confounding effects on other model behaviors, and the human-assigned grades in GraSD accurately reflect the model's internal representations.

What would settle it

If graded steering strength changes produce the same uniform boost and collapsed item variation as uniform steering, or if the resulting interpretive shifts show no correlation with the GraSD grades across models.

Figures

Figures reproduced from arXiv: 2604.07006 by Ye-eun Cho.

**Figure 2.** Figure 2: Graded changes in interpretive outcomes be [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗

**Figure 3.** Figure 3: Proportion of pragmatic interpretations for [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Scatter plots illustrating the relationship be [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Scatter plots illustrating the relationship between pragmatic similarity (x-axis) and logical similarity [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Histograms showing item-level changes in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗

**Figure 8.** Figure 8: Scatter plots illustrating the relationship be [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

**Figure 9.** Figure 9: Scatter plots illustrating the relationship be [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗

**Figure 10.** Figure 10: Scatter plots illustrating the relationship be [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗

**Figure 11.** Figure 11: Scatter plots illustrating the relationship be [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗

**Figure 12.** Figure 12: Item-level proportions of pragmatic interpretations for LLaMA3 across baseline, uniform activation [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗

**Figure 13.** Figure 13: Item-level proportions of pragmatic interpretations for Qwen2 across baseline, uniform activation [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗

**Figure 14.** Figure 14: Item-level proportions of pragmatic interpretations for Gemma2 across baseline, uniform activation [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗

**Figure 15.** Figure 15: Item-level proportions of pragmatic interpretations for OLMo across baseline, uniform activation steering, [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗

**Figure 16.** Figure 16: Scatter plots illustrating the relationship between pragmatic similarity (x-axis) and logical similarity [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗

**Figure 17.** Figure 17: Scatter plots illustrating the relationship between pragmatic similarity (x-axis) and logical similarity [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗

**Figure 18.** Figure 18: Scatter plots illustrating the relationship between pragmatic similarity (x-axis) and logical similarity [PITH_FULL_IMAGE:figures/full_fig_p016_18.png] view at source ↗

**Figure 19.** Figure 19: Scatter plots illustrating the relationship between pragmatic similarity (x-axis) and logical similarity possible-certain p scarce-unavailable g adequate-good g snug-tight py small-tiny [PITH_FULL_IMAGE:figures/full_fig_p017_19.png] view at source ↗

**Figure 20.** Figure 20: Histograms showing item-level changes in [PITH_FULL_IMAGE:figures/full_fig_p018_20.png] view at source ↗

**Figure 22.** Figure 22: Histograms showing item-level changes in [PITH_FULL_IMAGE:figures/full_fig_p018_22.png] view at source ↗

read the original abstract

Pragmatic inference is inherently graded. Different lexical items give rise to pragmatic enrichment to different degrees. Scalar implicature exemplifies this property through scalar diversity, where implicature strength varies across scalar items. However, evaluations of pragmatic inference in large language models (LLMs) often rely on prompt-based manipulations. Beyond prompt-level effects, this study introduces Continuous Interpretive Steering (CIS), a method that probes graded pragmatic interpretation by treating activation-level steering strength as a continuous experimental variable. To support this analysis, this study introduces a new dataset, GraSD, which encodes graded scalar diversity. Experiments on four LLMs show that uniform activation steering increases pragmatic interpretations globally but collapses item-level variation, whereas graded activation steering yields differentiated interpretive shifts aligned with scalar diversity grades. It indicates that graded sensitivity is encoded in the representation space and can be systematically recovered through controlled intervention. Together, CIS and GraSD provide a principled framework for evaluating graded pragmatic sensitivity in LLMs.

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.

Desk Editor's Note private letter to a colleague

The paper adds continuous activation steering strength as a variable for probing graded scalar implicatures and pairs it with a new graded dataset, but the results do not yet rule out broad behavioral side effects.

read the letter

The main thing here is that the authors treat activation steering strength as a continuous experimental variable rather than a binary on/off switch. They pair it with a new dataset called GraSD that grades scalar items by how strongly they trigger implicatures. The experiments then compare uniform steering, which lifts pragmatic responses everywhere but flattens the differences between items, against graded steering, which produces shifts that line up with those grades. This setup is new enough in the context of pragmatic evaluation. Most prior work sticks to prompt variations, so adding a representation-level intervention with tunable strength is a step forward. The results on four different LLMs give some indication that the model encodes these graded sensitivities in a way that can be accessed and modulated. The paper does a decent job of showing the contrast between the two steering regimes. That contrast is the clearest part of the work. Where it gets soft is on the interpretation of what the graded steering is actually doing. The claim that this recovers internally encoded graded sensitivity assumes the intervention is relatively clean. But as the stress-test points out, there could be broader changes in how the model generates text at different strength levels that happen to produce the observed pattern without touching the specific pragmatic representations. The summary does not mention any measurements of perplexity, output diversity, or performance on control tasks at the same steering strengths. If those were included in the full paper, they would help a lot. Otherwise the central result stays provisional. This kind of paper is aimed at researchers who care about mechanistic accounts of pragmatic inference in LLMs. It is not a complete demonstration, but the method and the dataset are concrete enough that a serious referee could evaluate whether the controls are sufficient and whether the statistical patterns hold up. I would send it out for peer review.

Referee Report

2 major / 1 minor

Summary. The paper introduces Continuous Interpretive Steering (CIS), which treats activation steering strength as a continuous variable to probe graded pragmatic inference (specifically scalar implicature) in LLMs. It also presents the new GraSD dataset encoding graded scalar diversity across items. Experiments on four LLMs are reported to show that uniform steering increases pragmatic interpretations globally while collapsing item-level variation, whereas graded steering produces differentiated shifts that align with the GraSD grades; this is taken to indicate that graded sensitivity is encoded in the representation space and recoverable via intervention.

Significance. If the central experimental claims hold after addressing controls, the work supplies a new activation-level intervention paradigm for studying graded pragmatic phenomena in LLMs that goes beyond prompt engineering. The GraSD dataset is a concrete, reusable resource that could standardize evaluation of scalar diversity. These elements would strengthen the toolkit for mechanistic interpretability of pragmatics, provided the method isolates the intended representational effect.

major comments (2)

[Abstract / Experiments] Abstract and experimental description: the claim that graded steering produces differentiated shifts 'aligned with scalar diversity grades' and thereby shows encoded graded sensitivity is load-bearing for the paper's conclusion, yet the text provides no evidence of controls for nonspecific effects of steering strength (e.g., changes in perplexity, output entropy, response length, or accuracy on non-pragmatic tasks at matched strength levels). Without such checks, the alignment could arise from broad distributional shifts rather than item-specific recovery of internal representations.
[Method] Method section: the precise definition of 'graded activation steering' (how multipliers are assigned per item from GraSD grades, which layers or heads are steered, and whether strength variation was pre-registered) is not detailed enough to evaluate whether the intervention cleanly modulates the targeted sensitivity or introduces confounds that could artifactually produce the reported differentiation.

minor comments (1)

[Abstract] The abstract would be clearer if it stated the number of items in GraSD, the four specific LLMs tested, and the exact metric used to quantify 'pragmatic interpretations.'

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The two major comments identify important gaps in controls and methodological transparency that we will address through targeted revisions. We respond to each point below.

read point-by-point responses

Referee: [Abstract / Experiments] Abstract and experimental description: the claim that graded steering produces differentiated shifts 'aligned with scalar diversity grades' and thereby shows encoded graded sensitivity is load-bearing for the paper's conclusion, yet the text provides no evidence of controls for nonspecific effects of steering strength (e.g., changes in perplexity, output entropy, response length, or accuracy on non-pragmatic tasks at matched strength levels). Without such checks, the alignment could arise from broad distributional shifts rather than item-specific recovery of internal representations.

Authors: We agree that the absence of explicit controls for nonspecific steering effects weakens the ability to attribute differentiated shifts specifically to recovery of graded representations. The current manuscript relies on the contrast between uniform and graded conditions to argue for item-specific effects, but this does not fully rule out broad distributional changes at varying strengths. In the revised manuscript we will add analyses of perplexity, output entropy, response length, and accuracy on matched non-pragmatic tasks (e.g., factual recall and sentiment classification) across the same range of steering strengths used in the graded condition. These results will be reported in a new subsection of the Experiments section and referenced in the abstract. revision: yes
Referee: [Method] Method section: the precise definition of 'graded activation steering' (how multipliers are assigned per item from GraSD grades, which layers or heads are steered, and whether strength variation was pre-registered) is not detailed enough to evaluate whether the intervention cleanly modulates the targeted sensitivity or introduces confounds that could artifactually produce the reported differentiation.

Authors: We acknowledge that the current Method section is insufficiently precise on these operational details. In the revision we will expand the description to specify: (1) multipliers are computed by linearly scaling each item's GraSD grade to the interval [0, 1] and applying the resulting value as the steering coefficient; (2) steering is performed on the same set of layers and heads previously identified via activation patching on a held-out subset of scalar items; and (3) the decision to vary strength continuously was made after pilot experiments rather than through formal pre-registration, as the study is exploratory. These additions will allow readers to assess potential confounds directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity in experimental framework

full rationale

The paper introduces Continuous Interpretive Steering (CIS) as a new method and GraSD as a new dataset, then reports empirical results from activation steering experiments on four LLMs. No mathematical derivations, equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided abstract or described content. The central claim that graded sensitivity is encoded in representation space is presented as an interpretation of experimental outcomes rather than a reduction to inputs by construction. The work is self-contained as an empirical probe and does not rely on load-bearing self-citations or ansatzes smuggled from prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities can be extracted. The central claim implicitly assumes that activation steering strength maps monotonically to interpretive strength without side effects.

pith-pipeline@v0.9.0 · 5447 in / 1167 out tokens · 30657 ms · 2026-05-10T18:03:02.408101+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

[1]

Grains: Gradient-based attribution for inference-time steering of llms and vlms.CoRR, abs/2507.18043, 2025a

Olmo: Accelerating the science of language models. InProceedings of the 62nd annual meet- ing of the association for computational linguistics (volume 1: Long papers), pages 15789–15809. Bertram Højer, Oliver Jarvis, and Stefan Heinrich. 2025. Improving reasoning performance in large language models via representation engineering. In13th In- ternational C...

work page arXiv 2025
[2]

Interpretable steering of large language models with feature guided activation additions.arXiv preprint arXiv:2501.09929, 2025

Interpretable steering of large language mod- els with feature guided activation additions.arXiv preprint arXiv:2501.09929. Charles Spearman. 1987. The proof and measurement of association between two things.The American journal of psychology, 100(3/4):441–471. Fang-Yi Su, Gia-Han Ngo, Ben Phan, and Jung-Hsien Chiang. 2025. Cas: enhancing implicit constra...

work page arXiv 1987