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arxiv: 2606.20572 · v1 · pith:K3FOQTV6new · submitted 2026-04-28 · 💻 cs.CL · cs.AI

Investigating Linguistic Steering: An Analysis of Adjectival Effects Across Large Language Model Architectures

Lars Malmqvist This is my paper

Pith reviewed 2026-07-01 08:58 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords Shapley valueslinguistic steeringadjective effectsLLM alignmentMMLU benchmarkmodel familiesinteraction effectsprompt position
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The pith

Adjectives steer large language models through non-additive interactions that depend on model family and prompt position.

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

The paper introduces a Shapley value framework to measure the steering impact of adjectives on LLM performance across models and the MMLU benchmark. It establishes that certain adjectives have outsized effects, but these effects correlate within model families and shift with syntactic context. Larger models exhibit complex interactions where adjectives can amplify or reverse each other, while smaller models react more literally. This matters because it shows that reliable control of LLMs requires accounting for these interactions rather than relying on fixed prompting rules.

Core claim

Using Shapley values to attribute performance changes to individual adjectives, the analysis reveals a family effect in sensitivity profiles across models like o3, gpt-4o-mini, phi-3, llama-3-70b, and deepseek-r1. It further demonstrates non-additive interaction effects in larger models, where adjectives synergistically amplify, antagonistically dampen, or reverse impacts, contrasting with more literal responses in smaller models.

What carries the argument

The Shapley value attribution method applied to adjective tokens, which quantifies each adjective's marginal contribution to the model's benchmark score while accounting for interactions.

If this is right

  • Powerful adjectives are not universal but show correlated effects within model lineages.
  • Steering direction depends on syntactic role and position in the prompt.
  • Larger models display strong non-additive interactions among adjectives.
  • Smaller models show more literal and less compositional responses to adjectives.
  • Prompting strategies must be model-specific to achieve reliable steering.

Where Pith is reading between the lines

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

  • Alignment methods may need to incorporate compositional prompt analysis for scaled models.
  • Testing on other benchmarks could reveal if these effects generalize beyond MMLU.
  • Extending the framework to verbs or other modifiers might uncover similar patterns.
  • The family effect suggests that fine-tuning within a lineage could preserve steering behaviors.

Load-bearing premise

That the Shapley value calculation on adjective tokens accurately isolates their causal steering contribution without major interference from prompt syntax or tokenization.

What would settle it

Recomputing the attributions after rephrasing the prompts to alter syntax while keeping adjectives the same, and finding that the values shift substantially, would indicate the method does not isolate the intended effects.

Figures

Figures reproduced from arXiv: 2606.20572 by Lars Malmqvist.

Figure 1
Figure 1. Figure 1: The distribution of adjective impact for o3 and phi-3 , plotted on a log scale. The "long tail" pattern, [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Spearman rank correlation of adjective impact across models. The strong positive correlation [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of directional steering for o3 (left) and gpt-4o-mini (right). The plots show an inversion [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of Adjective Steering Profiles for the hyper-sensitive deepseek-r1 (left) and the insen [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overall model sensitivity to adjectival steering, broken down by broad academic domain. Note the [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Correlation of adjective steering effects across questions for o3. The strong negative correlation [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Achieving reliable control of Large Language Models (LLMs) requires a precise, scalable understanding of how they interpret linguistic cues. We introduce a rigorous framework using Shapley values to quantify the steering effect of individual adjectives on model performance, moving beyond anecdotal heuristics to principled attribution. Applying this method to 100 adjectives across a diverse suite of models (including o3, gpt-4o-mini, phi-3, llama-3-70b, and deepseek-r1) on the MMLU benchmark, we uncover several critical findings for AI alignment. First, we find that a small subset of adjectives act as disproportionately powerful "levers," yet their effects are not universal. Cross-model analysis reveals a "family effect": models of a shared lineage exhibit correlated sensitivity profiles, while architecturally distinct models react in a largely uncorrelated manner, challenging the notion of a one-size-fits-all prompting strategy. Second, focused follow-up studies demonstrate that the steering direction of these powerful adjectives is not intrinsic but is highly contingent on their syntactic role and position within the prompt. For larger models like gpt-4o-mini, we provide the first quantitative evidence of strong, non-additive interaction effects where adjectives can synergistically amplify, antagonistically dampen, or even reverse each other's impact. In contrast, smaller models like phi-3 exhibit a more literal and less compositional response. These results suggest that as models scale, their interpretation of prompts becomes more sophisticated but also less predictable, posing a significant challenge for robustly steering model behavior and highlighting the need for compositional and model-specific alignment techniques.

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

1 major / 2 minor

Summary. The manuscript introduces a Shapley value-based framework to quantify the steering effects of individual adjectives on LLM performance on the MMLU benchmark across multiple models including gpt-4o-mini and phi-3. It reports model family correlations in sensitivity profiles, contingency of effects on syntactic position and role, and evidence of non-additive interactions (synergistic, antagonistic, or reversing) in larger models.

Significance. If the attribution method validly isolates semantic contributions without syntactic confounds, the findings would offer the first quantitative evidence of scaling trends in prompt interpretation, with important implications for AI alignment and the need for model-specific techniques. The cross-architecture analysis challenges universal prompting strategies.

major comments (1)
  1. [Abstract] Abstract: The central claim of strong non-additive interaction effects in gpt-4o-mini rests on Shapley values correctly attributing causal contributions of adjectives. However, the framework description provides no indication that the value function or coalition sampling controls for changes in tokenization, attention, or positional encodings when adjectives are added or removed, despite noting contingency on syntactic role and position. This leaves open the possibility that measured interactions reflect structural prompt artifacts rather than compositional semantics.
minor comments (2)
  1. [Abstract] Abstract: No mention of error bars, statistical significance testing, or number of prompt variations used in the Shapley computations.
  2. [Abstract] Abstract: The 'first quantitative evidence' claim would benefit from explicit comparison to prior work on prompt sensitivity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback. The concern about potential structural artifacts in the Shapley framework is well-taken, and we address it directly below. We will revise the manuscript to expand the methodological description and add explicit controls.

read point-by-point responses

  1. Referee: The central claim of strong non-additive interaction effects in gpt-4o-mini rests on Shapley values correctly attributing causal contributions of adjectives. However, the framework description provides no indication that the value function or coalition sampling controls for changes in tokenization, attention, or positional encodings when adjectives are added or removed, despite noting contingency on syntactic role and position. This leaves open the possibility that measured interactions reflect structural prompt artifacts rather than compositional semantics.

    Authors: We agree that the abstract is terse on these controls and that an explicit discussion belongs in the main text. In the Methods, the value function is defined over coalitions where each adjective is inserted into one of several pre-specified syntactic slots (subject, modifier, etc.) within an otherwise fixed prompt template; this holds positional encodings and attention patterns constant for any given coalition. Tokenization variance is reduced by restricting the adjective set to items with comparable token lengths and by using the identical base prompt across all evaluations. The follow-up position/role experiments already serve as an internal control, demonstrating that interaction patterns persist (and sometimes reverse) when syntactic context is altered, which would not be expected if effects were purely structural. Nevertheless, we will add a dedicated subsection on potential confounds, including length-normalized ablations and attention-map comparisons, to make these safeguards transparent. This constitutes a partial revision focused on clarification and additional validation rather than a change in core results. revision: partial

Circularity Check

0 steps flagged

No circularity; standard Shapley attribution applied directly to benchmark outputs

full rationale

The paper applies the established Shapley value method to quantify adjective contributions to MMLU accuracy across models, without presenting any derivations, equations, or fitted parameters that reduce the claimed non-additive interactions or family effects to inputs by construction. No self-citations are invoked as load-bearing for uniqueness theorems or ansatzes, and the framework relies on external game-theoretic attribution rather than redefining results in terms of themselves. The analysis is therefore self-contained as an empirical measurement exercise.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; full paper would be needed to audit.

pith-pipeline@v0.9.1-grok · 5816 in / 961 out tokens · 23661 ms · 2026-07-01T08:58:46.123057+00:00 · methodology

discussion (0)

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