Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
Authors: Qingsong Wang
Chang Yao
Jingyuan Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Controllable Generation
Activation Steering
Style Control
Large Language Models
Primary area: generative models
TL;DR: We treat LoRA as a causal probe to extract a pure "style vector" from non-parallel corpora. This vector allows for continuous, bidirectional control over text style at inference time with almost no computational cost.

Abstract: Controlling stylistic attributes of Large Language Models (LLMs), such as formality or conceptual complexity, is crucial for effective human-AI interaction. However, current methods often suffer from discreteness, reliance on expensive parallel corpora, and instability, limiting their practical utility. This paper introduces a novel framework for robust activation steering that eliminates the need for parallel corpora, enabling continuous, fine-grained, and linear control over LLM outputs. Our key insight is to reframe Low-Rank Adaptation (LoRA) as a causal intervention tool. By contrasting activations on identical inputs with and without a LoRA perturbation trained via a contrastive objective, we separate the influence of content. To enhance reliability, we introduce a robust aggregation pipeline that uses Principal Component Analysis (PCA) for denoising and the geometric median for centrality estimation, yielding a stable and disentangled style vector. At inference, this vector allows for precise bidirectional control via activation steering with negligible computational overhead. We demonstrate state-of-the-art performance on controlling conceptual complexity, text detoxification, and formality control. Our method not only provides superior control but also generalizes across different models and tasks, and enables simultaneous multi-attribute control.

Published as a conference paper at ICLR 2026
CAUSAL-STEER: DISENTANGLED CONTINUOUS STYLE CONTROL WITHOUT PARALLEL CORPORA
Qingsong Wang', Chang Yao & Jingyuan Chen
Sccoool of Software Technology, Zhejiang University
ZZhejiang University
Corresponning authors
wwssong, changy, j ingguanchen) ez edu. cn
ABSTRACT
Controlling stylistic attributes of Large Language Models (LLMs), such as for- mality or conceptual complexity, is crucial for effective human-AI interaction. However, current methods often suffer from discreteness, reliance on expensive parallel corpora, and instability, limiting their practical utility. This paper intro- duces novel framework for robust activation steering that eliminates the need for parallel corpora, enabling continuous, fine-grained, and linear control over L.LM outputs. Our key insight is to reframe Low-Rank Adaptation (LoRA) as a causal intervention tool. By contrasting activations on identical inputs with and without LoRA perturbation trained via a contrastive objective, we separate the influence of content. To enhance reliability, we introduce a robust aggregation pipeline that uses Principal Component Analysis (PCA) for denoising and the geometric me- dian for centrality estimation, yielding a stable and disentangled style vector. At inference, this vector allows for precise bidirectional control via activation steer- ing with negligible computational overhead. We demonstrate state-of-the-as per- formance on controlling conceptual complexity, text detoxification, and formality control. Our method not only provides superior control but also generalizes across different models and tasks, and enables simultaneous multi attribbte control. Our code is available at: https: //gitttbbbbb.... com Stteer/s5///auaa-SeSee Steee
INTRODUCTION
Large Language Models (LLMs) are emerging as power- ful general-purpose tools, demonstrating remarkable po-
Existing Methods
tential in complex interactive scenarios that demand ac- curate information delivery (Brown et al.) 20200 Touvron (ct al. (002a).. Yet, their practical utility extends beyond factual correctness: effective human-Al interaction also requires fine-grained control over expressive dimen- sions of generated output, ranging from linguistic for-
Whate
Socal
Dieen Caper
Parzilll Eaputs Diseraie Outpuis
Our
Cantinunas Style Lincar Cuntred
mality to conceptual complexity. Such control is essen- tial for tailoring responses user's cognitive state, for example aligning explanations with their Zone of Proxi- mal Development (Vygotsky [1778. Current mainstream control paradigms, however, remain limited in both pre- cision and stability as show in Figure T Prompt engineer- ing (Liu et al. 2023) and instruction fine-tuning (Ouyang ct al. 2022) all operate in fundamentally discrete control spaces, restricting users to coarse categories (e.g., "begin- ner" vs. "expert") and producing unpredictable shifts dur- ing iterative adjustment. This discreteness is misaligned with the inherently continuous nature of many stylistic spectrums, such as knowledge granularity, ultimately constraining personalization and reducing interaction efficiency.
without Parallel Corpara Continusus, Elno-graindl and Sodircctional Style Central
Speckile
Comparison of LLM style control: existing methods require par- allel corpora for rigid, discrete control, whereas our method uses non-parallel data to achieve fine fine-grained, continuous linear control.
Figure 1:

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Published as a conference paper at ICLR 2026
To achieve continuous adjustment, some studies apply linear interpolation of model parame- ters (Kangaslahti & Alvarez- Melis 2025 Ilaarco et al. 2022). Using LoRA, a base model is fine- tuned toward different stylistic anchors, and the parameter updates are then interpolated. While this approach forms a continuous control space, it faces key limitations: it often requires parallel data for anchor training and tends capture corpus-specific artifacts rather than genuine stylistic differences. Consequently, the model merges dataset features instead of learning a transferable representation of style, resulting in outputs that are unstable and difficult to control.
To address these gaps, the emerging paradigm of activation steering has attracted increasing at- tention (Turner et al. 2023a Zhang et al. 2025). By directly manipulating a model's internal representations in latent space, activation steering offers a principled mechanism for continuous, fine-grained control over stylistic attributes. However, the practical effectiveness of this approach hinges on obtaining high-quality steering directions. Existing methods typically rely parallel corpora(Han et al., 2024), pairs of texts aligned in content but differing in style, which are expen- sive to construct and rarely available for complex dimensions such as conceptual complexity. This dependence introduces two critical challenges. First, the content pollution problem: imperfect content alignment causes extracted difference vectors to encode both stylistic and semantic varia- tion, reducing generalizability. Second, the robustness problem: even when initial style signals are isolated, steering directions remain vulnerable to noise and outliers, limiting stability across topies.
In this work, we present a novel framework, Causal-Steer, for robust, corpus-free activation steering reframe Low-Rank Adaptation (Hu)
that enables precise and linear style control. Our key insight iet al. 2022) (LoRA) as a causal intervention tool. By contrasting activations from identical inputs with and without LoRA perturbation, we isolate the net stylistic effect while circumventing the need for parallel data. To further enhance reliability, we design an aggregation pipeline that applies PCA for denoising and employs robust centrality estimation derive stable steering vectors resilient to outliers. Using conceptual complexity as case study, we demonstrate that our method supports continuous bidirectional control and generalizes across tasks and languages.
Our main contributions are as follows:
1. Linear, Corpus-Free Bidirectional Control. We extract a style vector from a single, non- parallel dataset (even single-style dataset) and leverage it to realize fine-grained, linear, and bidirectional control, removing the need for costly parallel corpora.
2. Robust and Disentangled Representation. Through PCA-based denoising and robust central- ity aggregation, our method suppresses content-related noise and resists outliers, producing a disentangled style vector that remains stable and transferable across diverse settings.
3. Versatile, ttate-of-the-Art Performance. Causal -Steer outperforms prior methods on multiple tasks such as text detoxification and formality control, and consistently generalizes to different domains and languages without additional tuning.
2 RELATED WORK
Approaches to style control LLMS can be broadly categorized into modifications of model param-
eters (Mañas et al., 2025 Feng et al. 2025) and inference-tine time activations (Feng al. 2024 Klein
& &Nabil 2024). In the parameter space, Ilharco et al. (2022) introduced "task arithmetic" where vec- tors derived from fine-tuning are used edit model capabilities (Akiba al. 2025). More directly related to style, Dekoninck et al. (2023) demonstrated that interpolating between multiple LoRA adapters, each fine-tuned on different attributes, can effectively control generation style. An alter- native, more lightweight paradigm is activation engineering. Pioneering this, Turner et al. (2023a) developed Activation Addition (ActAdd), computing a steering vector from the activation difference of single pair of contrasting prompts. This concept was refined by Rimsky et al. (2024) with Con- trastive Activation Addition (CAA), which averages these differences over large dataset of pairs for greater stability. In similar vein, Zhang et al. (2025) proposed Generation with Concept Activa- tion Vectors (GCAV), deriving a controlling vector by training a linear classifier on activations. For more targeted edits, Li et al. (2023) developed Inference- Time Intervention (TTI) to shift activations in specific truth-related attention heads. Finally. Zou et al. (2023) unified these activation- approaches under the conceptual framework of Representation Engineering.
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Contrasfivel-Coeidee LoRA
Extracting Pstyle via Causal Intervention
Inference via Activation Steering
D2
Dj
C
base
fi(Mbase: di)
Bf
Bif
Novice
Specialist
M base
formaall-44)
formal
Af
Ast
Aggregation and Demoising
Av () formal.
Inference
add
Countaativv
m
arg min
milla
m
ATTRAAT DMRAAO
style
P
m.
Pailfre
Negithro
X
sstyle
lsteer
Framework for extracting and controlling style. The process is (1) training style- specific LoRAs with a contrastive loss, (2) extracting perturbations caused by the LoRAs and aggre- gating them into Esyle. and (3) using this vector for continuous style control at inference. Here, Dj and Da denote the formal and informal datasets; B,, A,, Br, and Agr are the corresponding LoRA modules; Mome is the base model; fi(-) is the activation extraction function; Laeer is the steering layer; (v is the per-example activation perturbation; and Vayle is the aggregated style vector.
Figure 2:
3 METHODOLOGY
To separate the extracted style vector Vayle from content and mitigate interference from style noise, we aim achieve fine- fine-grained linear control over text style. Causal-Steer as show in Figure 2 consists of three main stages: 1) We use contrastively-guide LoRA to introduce a precise, low- rank perturbation to the base model's weights and extract style-specific activation differences from the perturbation. 2) We introduce robust aggregation technique that combines PCA with ro- bust centrality estimation
denoise the extracted vectors and identify the core style direction. 3) At inference time, we use the normalized style vector for multi-layer activation steering, enabling continuous and fine- fine-graing grained style control.
3.1 PRELIMINARIES
Base and Perturbed Models. Musse is frozen pretrained language model. Mteeaa and Mintonal are models obtained by applying LoRA-based perturbations to M trained style-specific datasets Dioemll and Dinfomaal respectively, using contrastive loss. Crucially, these datasets need not be parallel, the only requirement that they exhibit contrasting styles.
MLP-Layer Activation Extractor. For a Transformer layer a model M, and a text d, we define fi(M, d) as the mean of the MLP output vectors for all generated tokens, excluding prompt tokens:
T JiMM, d) T,
where x," is the post-MLP hidden state for the t-th token, and T, is the number of generated tokens. This design ensures that the extracted style vector reflects holistic stylistic features throughout the model's output, rather than structural artifacts from the prompt or the partial semantic biases of individual tokens.
3.2 EXTRACTING STYLE VECTORS VIA CAUSAL INTERVENTION
LoRA fundamentally applies a precise, low-rank update AW to the base model's weights Wo. re- sulting in fine-tuned weights Wayte Wo AW. We treat this fine-tuning process precise causal intervention on the model. Its goal is to elicit specific stylized behaviors while avoiding significant shifts in the semantic space learned during pretraining. To ensure this perturba- tion AW effectively separates style from content, we guide its training with contrastive learning objective. This objective directs the weight update towards subspace that maximizes style separa-
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bility. Specifically, the objective function to guide the Morral perturbation is:
exp(sim(ha, be,)/T) log exp(sim(ha,, ha,) + Ed. exp(sim(ha, h/,
Leontrastive E
where the anchor ha, and positive example hé, are from Diomal. and negative examples ha, are from Dinformal- This discriminative guidance compels the LoRA update AW to focus on generaliz- able features that can distinguish the two styles across different content. Consequently, the model must suppress content-related activations, thereby learning pure and generalizable style represen- tation. We employ a differential method to extract the pure effect of this stylistic perturbation. For a formal sample d, and layer I, the stylistic perturbation vector is defined as:
Av
(1) formal,i [a d:) Ji (Masse dz).
The informal difference vector Av (
informal. is calculated analogously.
This differential measurement approach is fundamentally different from naive observational meth- ods. A baseline approach would be to directly obtain the style vector from the difference in acti- vations on the datasets Diomal and Dinformal within the base model Musee However, this method assumes that content-related information can be eliminated via vector subtraction, thereby isolating the pure style signal. This assumption holds only under the condition of extremely high content consistency between texts, which in necessitates meticulously crafted parallel corpus. Causal- Steer, in contrast, captures the L-indundeeed perturbation on the very same input di elegantly circumventing this reliance on parallel corpora.
Causal-Steer's ability to isolate the stylistic effect hinges on the approximately linear relationship between the weight perturbation AW and the change activations. We formalize this relationship by considering the activation of layer I function of weights W and data d, denoted as h (WWWW)) A first-order Taylor expansion around the base weights Wo yields:
Ah 1 (d) ( (Wo + AW, d) h (2) (Wo, d) Jn,w (.o.(W.)) d) AW, where Jn, w (Wo,d) d)- space to changes in the activation space.
(4)
( (W.)) w=w, is the Jacobian matrix mapping perturbations in the weight
BW
This linear approximation indicates that the Ah we extract can be interpreted as the image of the LoRA-induced weight perturbation AW under this Jacobian mapping. In other words, Ah(!) represents how the stylistic intervention in the weight space manifests within the model's hidden representation space, thus providing direct and disentangled handle for controlling style.
3.3 ROBUST AGGREGATION AND DENOISING
Our objective is to aggregate the collected sample-level difference vectors for each layer into a sin-
(1) gle, robust style vector "tylee A simple baseline, the arithmetic mean, is suboptimal as it conflates the primary style signal with sample-specific content variations. This indiscriminate averaging in- troduces noise and fails to isolate the core style direction. To address this, we propose two-stage strategy that first denoises the vectors to isolate the style subspace and then performs a robust aggre- gation within that subspace.
Vector Set Construction and Modeling. We first construct a unified set of style difference vectors by aligning their directions. Specifically, we negate the vectors derived from the informal style to align them with the formal style direction:
( = (Av!!) formal u(-Av ( I
(5)
We model each vector x; E ( as composition of a shared, low-dimensional style signal v and high-dimensional, sample-specific content noise :
style
(
style
Coontent,
This model posits that the consistent style direction is the primary signal shared across all samples, whereas content-related features manifest as diverse noise.
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Table 1: Evaluation results conceptual complexity. Methods with the mean suffix utilize mean token feature extraction for control. Among these, RepEmean failed and is excluded from the ranking, CS denotes our Causal-Steer method, and CSsing denotes the variant trained only on the single sided (simple) dataset. For all successful methods, the best results are in bold and the second best are underlined. Arrows (1:) indicate the desired direction for each metric. All ChatGPT-4. bbaed metrics are scored on 1-10 scale.
Method
Model
Complex Diff.
Simple
Acc.
SMOG.
C-I.t
Rel.t
Fla.r
Acc.t
Diff.
F-G.1
SMOG.1
C-I.j
CAA m RepE
Qwen 2557B
9.56
8.71
8.19
5.37
16.28
15.57
15.64
9.57
8,51
7.58
4.18
13.64
11.19
15.71
9.75
9.08
8.48
5.16
16.64
15.05
15.45
9.67
8.73
7.83
4.17
13.78
17.48
17.14
Instruct
5.35
14.84
8.73
17.23
CAAsca
9.53
8.26
7.91
8,00
19.98
19.24
29.08
9.01
8.36
6.59
2.85
11.97
12.07
10.81
RepErea
9.81
9.12
8.41
4.99
16.57
15.67
17.59
9.83
9.13
8.68
4,82
14,98
15.00
14.41
Owen
7.87
6.71
12.52
CLMI
2.57B
8.92
7.97
7.60
8.36
20.67
22.49
26.91
9.50
9.06
7.644
3.47
11.38
11.37
10.02
ReFT
Instruct
8.96
8.31
7.78
4.11
12.38
14,20
13:46
8.26
7.91
6.87
3.05
12.24
11.23
9.89
N.42
31.55
8,41
CSugg
9.16
8.34
CAArua
8:35
6.91
5.53
6.62
20.04
19.68
21.60
6.64
4.89
4.01
1.97
12.69
10.30
9.98
Replinua
14.45
Mhaa
LLaMMa
5.98
18.65
19.88
4.98
3.74
2.08
8.27
10.04
9.42
CLMI
3.1-88
9.17
8.28
8.15
6.63
19.00
20.05
26.86
9.41
9.29
7.26
2.96
9.54
10.69
8.67
ReFT
-Instruct
7.29
7.25
6.30
3.12
3.12
19.19
8.15
8.05
8.80
5.71
2.31
7.31
9.76
7.27
7.22
6.58
8.02
CS.ak
9.59
8.31
2.94
7.81
21.63
20.17
24.64
8.04
7.61
5.18
2.16
9.04
10.22
8.79
Denoising via PCA. Based on our model, we hypothesize that the shared style signal (1)) tutes the principal components with the highest variance in the set ( Conversely, the content noise E is distributed across the remaining components of lower variance. Consequently, we employ PCA to separate the style signal from the content noise. We project each vector x; in X(I
syle consti-
onto the subspace spanned by the top k principal components:
X, = (P(O)
where the columns of (() jection acts as filter, preserving the low-dimensional style information while discarding high- dimensional content variations. Empirically, we find that a small value such as k = 8 is sufficient to capture the core style variance, supporting our hypothesis that style is low-dimensional attribute.
A are the top eigenvectors of the sample covariance matrix. This pro-
Robust Aggregation with Geometric Median. While PCA removes structural noise, outliers from atypical samples may persist within the projected style subspace. To mitigate their influence, perform a robust aggregation using the Geometric Median m!). which defined as:
m arg min Σ
E m
Unlike the arithmetic mean, the geometric median provides robust measure of centrality that is less sensitive to extreme values. Finally, we project the aggregated vector back to the original activation space obtain the definitive style vector:
m
style
This two-stage procedure yields robust style vector that generalizes across diverse inputs, enabling reliable and controllable style steering at inference time.
3.4 STYLE CONTROL AT INFERENCE VIA ACTIVATION STEERING
A key advantage of Causal-Stedd is its ability to enable bidirectional control from single style vec- tor. This vector can be extracted from a non-parallel corpus representing only the target property complex concepts), yet it can steer generation towards away from that property. We achieve this control at inference time via activation steering, a method that directly modifies a model's 's inter- nal activations without altering its weights.
Specifically, for a selected set of layers Lueer (see Section 4.3 for selection details), we intervene in the output of the MI.P submodule during the generation of each token t. We add the pre-computed,
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Per formsecce of Ours Complexity
Performacce of CAAmenn for Complexity
Perfomaance MI Complexity
to
10.0
Scares
Scorcs
500
Kinso
Sdratfic Acconcy
Toul Appaptinnce Demuliy
Scientt Acceao
40
40
20
Performence of Ours Fomulity
Performance CAAmean for Formality
Perfomuance Formality
2.5-
Scores
Scoress
Aupaptaenee Deeuliy
Tou
Temalit
40
20
20
40
20
20
40
0.0
Figure 3: A comparison of the control effectiveness of Causal-Steer against two baselines Com- plexity and Formality tasks. The results demonstrate that by adjusting the coefficient a, Causal-Steer effectively steers the target style while maintaining high scores for Relevance and Fluency.
normalized style vector
(I) styll to the original activation z, (!) scaled by an intensity coefficient a:
Zt
+
style
(10)
style
Here, (1)
Vsyle is the robust style vector derived previously. We normalize it to ensure that the interven- tion strength determined solely by the tunable scalar a.
The coefficient provides continuous and fine-grained control over the output's complexity level. A positive a guides the model towards the target property (e.g., complex). whereas a negative a steers the activations in the opposite direction (e.g., simple). When = O. the original model behavior is recovered as the activations remain unchanged. This method transforms complexity control into simple
single, interpretable parameter. Since style vectors are pre-computed, the intervention vector addition with negligible computational overhead. This makes the method highly efficient and flexible, allowing for smooth interpolation of concept complexity without any model retraining.
EXPERIMENTS
Datasets. We evaluate Causal-Steer on three stylistic control tasks: conceptual complexity, toxicity detoxification, and formality control. For our primary task of conceptual complex- ity, extract style vectors from the Scale dataset (Wang et al., 2025) and evaluate on the
Table 2: Ablation study results on the Qwen2.5- Instructt model.
Complex
Simple
Method
Acc.
Diff.
Acc.
Diff.
Causal-Steer
8.35
8.59
8.42
8.41
6.85
2.77
-wlo Contrast
8.24
8.50
8.24
8.31
6.87
3.01
ELI5 dataset (Fan et al. (019). To assess gen- eralization, we test toxicity control using vec- tors from APPDIA (Atwell et (022) on the TrroxiittPromppl corpus (Gehman et al. 2020), and formality control using data from Zhang et al. (2020) evaluated on ELI5. Further details
Difference
8.21
7.95
7.90
8.50
6.67
2.85
-wo Mean Token
7.41
7.28
4.26
8.94
8.24
4.73
-wo
8.18
8.27
2.81
-wo Mean
8.28
8.48
8.24
8.36
6.61
2.82
on data preprocessing and statistics are provided in the Appendix D
Baselines. We benchmark Causal-Steer against a comprehensive suite of baselines including Rep- resentation Engineering (RepE) (Zou et al. 2023), Contrastive Activation Addition (CAA) (Turner
ct al. 2023a). Inference- Cicrence-Time Intervention (ITI) (Li et al. 2023). Continuous Language Model In- terpolation (CLMI) (Kangaslahti & Alvarez-Meln 2025). ReFT (Wu et al.), POSPROMPT, Arith- metic (Dekoninck et al. 2023), ActAdd (Turner et al.) 2023b), and GCAV-Output (Zhang et al.
2025
Further details on each baseline are provided in the Appendix
Evaluation Metrics. For conceptual complexity control, we assess Relevance, Fluency, Scientific Accuracy, and Knowledge Difficulty using ChatGPTT-411 (OpenAl 2025), supplemented by the au-

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Published as a conference paper at ICLR 2026
tomated Flesch Grade Level (Flesch 2007). SMOG (Mc Laughlin 1969). Coleman (Coleman & Liau 1975). and human evaluation in the Appendix For formality control, we evaluate Tone Approprateness, Relevance, Fluency, and Formality using GPT-4.1. We also use the popular s- mode S from Hugging Face as the text formality style prediction model. This is binary classification model, and we use the probability value as the text formal style strength. Finally, detoxification performance is measured by the maximum toxicity score from the Perspective AP
hiprroberra-base-fommiltty--
Full evaluation prompts are detailed in the Appendix G
For the ChatGPT-4.1 metrics, we conducted three measurements and reported their average. Because the ChatGPT-4.1 scores were highly consistent, the standard deviations across the three runs were very small. Due to space constraints, we report only the average values and omit the standard deviations. All scores range from 1 to 10.
4.1 MAIN RESULTS ON CONCEPTUAL COMPLEXITY CONTROL
Table presents the quantitative re- sults for controlling conceptual com- plexity. Causal-Steer significantly outperforms most baselines across both the 2wwnn:5577B-Insrruct(000
Arorge Some Datrop
eet al. 2024) and I.LaMa3. 1-8B-
Instruct(Gratttfior et al., 2024). The
framework demonstrates strong style control while maintaining high gen- eration quality. When steering to- wards "Complex", Causal-Steer hibits superior control. On Qwen2.5- 7B, for instance, it achieves the high- est difficulty score (8.42) and Flesch grade (22.78), producing conceptu- ally advanced content without sac- rificing relevance or fluency. Con- versely, for the "Simple" condition, Causal-Steer consistently records the lowest difficulty and Flesch grade scores. These results confirm our vector's capacity for precise bidirec- tional steering.
Arrnge Spore
Coimclade
Figure 4: Heatmap of average style control scores across layer blocks. The y-axis and x-axis represent the start and end indices for the intervention block, respectively. Scores are averaged over multiple runs.
The analysis of the baselines reveals the importance of our activation strat- egy. Methods like the original CAA, ITI, and RepE rely on activations from the final token. This approach necessitates high-quality parallel corpora to be effective. As shown in the table, adapting CAA use mean activations (CAAmean) substantially enhances its control capabilities. In contrast, RepEmean still fails. We attribute this failure to its use of PCA for analyzing activations, technique that requires a much larger dataset to identify a meaningful style direction. This hypothesis is sup- ported by our generalization experiments, where its performance improves on the larger Formality dataset (10,00 examples) (Zhang et al. 2020).
Although CLMI achieves commendable results in style control, its primary limitation is a failure to disentangle style from content during fine-tuning. Consequently, the model learns to associate the target style with specific content from its training data, leading to the generation of content artifacts. CLMI's outputs are consistently shorter (approx. 100 tokens) than those of other methods (approx. 500 tokens), mirroring its training data in both length and structure. This highlights that Causal- Steer succeeds by isolating a pure style vector. This isolation enables robust, generalizable control, which is unachievable by other methods that suffer from severe content-style entanglement.
http::/hhigiiiiiiieoosss/////hbr https:/peerseetiivapp.co
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4.2 BANGLE-SOURC BIDIRECTIONAL CONTROL
Beyond the standard setting with two style-specific LoRA adapters, Causal-Steer also supports bidi- rectional control when both the adapter and the steering vector learned from single-sided cor- pus. To verify this, we construct a variant, denoted aaggee that uses only the Simple subset of the Scale dataset. In this variant, we remove the contrastive loss used in Section Ban and fine-tune single LoRA on Simple answers with the standard supervised objective. We then apply the same causal- intervention procedure
extract one style vector. At inference time, we use a positive steering coefficient a >> 0 for the learned style and negative coefficient < 0 to steer in the opposite (more complex) direction. As shown Table I Sisingle achieves remarkable performance, effectively demonstrating robust bidirectional control even when trained on single-sided data.
We further compare this single-source setting against ReFT. While ReFT performs well when steer- ing in the learned direction (positive scaling), it fails significantly when attempt bidirectional control. Specifically, applying a negative coefficient for ReFT does not yield the opposite style; instead, it leads to severe generation artifacts, such repetition and hallucinations, without suc- cessfully shifting the style. We hypothesize that this failure stems from the nature the learned representation: ReFT likely learns vector representing the residual between the base model and the dataset outputs, rather than an intrinsic style vector. Consequently, simply reversing this residual semantically meaningless, causing model collapse. In contrast, our method explicitly learns the style vector itself, thereby enabling stable and valid bidirectional control.
4.3 ANALYSIS OF THE CONTROL MECHANISM
To better understand the properties of Causal-Steer, we conduct deeper analysis of its control linearity and layer-wise sensitivity.
Linear and Stable Stylistic Con- trol. core objective of Causal- Steer is to enable continuous and predictable control over style. Fig- ure evaluates this by plotting per- formance metrics against the steering coefficient a. The results for Causal- Steer demonstrate a strong, approxi- mately linear relationship between a and the target style metrics, Formal- ity and Knowledge Difficulty. As a increases, the intended stylistic inten- sity grows predictably. Critically, this control achieved with minimal im- pact on generation quality. Key met- rics such as Relevance and Fluency remain high across wide operational range of a values. show- ing degradation only at extreme settings. This stability highlights the effectiveness of our robust aggregation pipeline.
Kocrdolge pillicully Sorre
Pormullty Score
Figure 5: Two-Dimensional Stylistic Control. Model out- put scores for (a) Knowledge Difficulty and (b) Formality under simultaneous steering.
The CLMI reveals more fundamental limitation. Its control is effectively confined to the spectrum between its two training endpoints, corresponding safe operational range for a between 0 and 1. Because this method functions by interpolating between the weights of two fine-tuned models, cannot extrapolate beyond the styles observed during training. Attempting to push the model outside this bounded range, for instance, by setting a to values like -1 or 2, results in catastrophic collapse in generation quality, causing all performance metrics to plummet. This means the method cannot generate content that is, for example, more formal than the examples in its formal dataset without sacrificing coherence. This fundamentally restricts its utility to narrow style range and prevents true creative or intensified style generation. The comparison confirms the superiority of Causal-Steer in creating a genuinely continuous, wide-ranging, and robust control mechanism.
Identifying Optimal Layers for Intervention. We investigate which layers are most influential for style control to inform an optimal intervention strategy. Figure 6 visualizes the average style score achieved by applying steering vector across different continuous blocks of layers, defined
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by start and end index in Qwen2.5-7B. Instrucuc.. When calculating the average score, we applied penalty for outputs that collapsed into repetitive, meaningless content. The heatmap reveals clear pattern: the most effective control (indicated by the highest positive scores in orange and red) is concentrated in the mid-to-late layers of the model. Specifically, interventions starting around layer 18 and extending approximately layer 23 yield the strongest stylistic effect. Intervening in the initial layers (1-10) proves far less effective and can even be detrimental to the output. This empir- ical result aligns with the prevailing hypothesis that later transformer layers encode more abstract semantic and stylistic information (Zhang et al. 2025). Based on this analysis, we apply steering to layers 18 through 23 in our main experiments, as this range provides robust and powerful locus for style manipulation.
4.4 ABLATION STUDIES
We conduct an ablation study to vali- date the necessity of each component in Causal-Steer. As shown in Table[] every component is critical for opti- mal performance.
Table 3: Performance of different methods on Formal and Informal datasets.
Method
Infermal
Rel.t
Tone-r
For.
RFFt
Rel.r
Hu.1
For.
P...
CAAren
9,05
6.25
8.32
6.83
01.93
9.28
8.08
5.65
2.30
0.32
Removing the contrastive learning objective ("-w/o Contrast") causes a notable decline in control effective- ness. Forgoing the differential acti-
RepfE
9.70
8.47
8.82
6:38
0.96
8.90
6.68
5.50
2.80
0.72
ITE
9.73
8:26
8.29
5,86
0.91
7.54
5.12
4.71
2.71
0.78
CLMI
2,50
9.37
9.41
7.58
01.88
8.82
7,70
5.94
2.40
0.36
9.84
9.38
0.20
vation ("-w/o Difference") also measurably weakens control. Both results validate our causal in- tervention strategy. The aggregation pipeline is equally important: removing PCA ("-w/o PCA") or using a simple arithmetic mean ("-w/o Mean"" degrades performance by failing to properly denoise the style signal. Finally, using only the last token's representation ("-w/o Mean Token") instead of averaging across all tokens severely diminishes control intensity, underscoring the need for holistic style signal. These findings confirm that all components contribute synergistically to the framework's 's effectiveness.
4.5 MULTI-ATTRIBUTE CONTROL
Causal-Steer's capabilities extend beyond single-attribute manipulation to natural, multi- dimensional style control. This theoretically grounded in the sparse nature of high-dimensional vector spaces, which permits the linear superposition of multiple style vectors (Liang et al.) (024)) By simply adding the vectors for different attributes, we can achieve simultaneous and composable control over the generation process.
Figure provides an empirical demonstration of this principle, where we jointly steer for Formality and Conceptual Complexity.. The figure presents two heatmaps showing the resulting style scores as function of the formality coefficient @a (horizontal axis) and the knowledge coefficient 0:2 (vertical axis). Specifically, Subfigure (a) illustrates the model's output Knowledge Difficulty Score, while Subfigure (b) shows the generated Formality Score. The heatmaps demonstrate that as the control signals on1 and (22 are varied, the corresponding style scores undergo a smooth and continuous transition. Furthermore, the plots reveal a positive correlation between the two attributes. Intensi- fying style's is control signal produces corresponding change in the score for the other style. For example, increasing the conceptual complexity signal (a:2) also tends to increase the formality score, and vice versa. This observation highlights the inherent semantic interplay between advanced con- ceptual content and formal language. The smooth and predictable gradients across both heatmaps provide strong evidence that Causal-Steer enables fine-grained and continuous control over multiple stylistic dimensions, while effectively capturing their natural associations.
4.6 GENERALIZATION TO OTHER CONTROL TASKS
To assess the generalizability of Causal-Steer beyond conceptual complexity, we evaluated its per- formance on two distinct stylistic control tasks: formality control and text detoxification.
Table presents the results for Formality task. Causal-Steer demonstrates superior control over the target style, achieving the highest formality score (8.61) when steering towards formal style and

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one of the lowest scores (2.22) when steering towards informal style. Crucially, this high degree of stylistic control is attained while simultaneously yielding the highest scores for relevance and sci- entific accuracy, indicating that Causal-Stess effectively modulates style without degrading content quality.
Furthermore, we applied Causal-Steer the critical safety task of detoxification with
Table 4: Comparison of toxicity control methods on two test sets. Toxicity (lower is better) and per- plexity (PPL, lower better) are reported.
Llama-2-7b-chat (Touvron al., 2023b), with results shown in Table 4 Causal-Steer sig- nificantly outperforms all baselines, achieving the lowest toxicity scores on both the toxic and random test sets, where both evaluations involve one-way detoxification rather than the bidirectional control used in our other exper- iments. This detoxification performance is achieved while maintaining a competitive per- plexity (PPL), suggesting that Causal-Steet ef- fectively mitigates toxicity without severely compromising the model's linguistic coherence. These experiments collectively demonstrate that our proposed vector extraction and steering mechanism provides a versatile and effective solution for a wide range of attribute control problems. We also tested Causal-Stecs rooss-inguually detailed cases are in Appendix J
Toxicity
Toxicity
Model
Toxicity
PPL
Toxicity
PPL
Baseline
0.1807
13.71
0.0956
19.23
POSPROMPT
0.1913
59.98
0.1008
18.32
ActAdd
0.1620
34.08
0.0852
12.61
Arithmetic
0.1625
6.84
0,0816 16
7.34
GCAV-Output
0.0879
21.29
0.0622
6,08
Causal-Steer 0.0609
12.95
0.0520
8.18
Table 5: Comparison of Model Responses
Question: Do magnetic and/or electric field have any influence on time and space?
10 Yes.... Electric and magnetic fields do not directly change spacetime, but they can affect how particles move, which influences how time and distance are measured in practice...
-5 Yes.... general relativity, electromagnetic fields contribute to spacetime curvature through their energy, meaning that strong fields can slightly alter the geometry of spacetime...
-20 Yes.... Within the framework of Einstein's general relativity, the interaction between electro- magnetism and gravity is formally described by the Einstein-Maxwell equations, where the energy tensor of the electromagnetic field directly enters Einstein's 's field equations to determine spacetime curvature...
4.7
CASE STUDY
Table 5 iillustrate representative responses under different control strengths As decreases, the generated answers gradually shift from intuitive and accessible descriptions to more theoretical and domain-specific explanations, ranging from practical comments measurement effects (a 10) to discussions of spacetime curvature (a = -5), and finally to highly academic references such the Peassein-Maxwer equations (a -20). This demonstrates that a serves continuous control knob, enabling smooth adjustment of responses along spectrum from layperson-friendly to technical. For brevity, we only present condensed example here, while the complete outputs and additional cases are provided in the Appendix H
5 CONCLUSION
We introduced Causal-Steer, novel framework for fine-grained style control LLMs that removes the dependency on parallel corpora by leveraging LoRA a causal intervention to disentangle style vectors. Supported by a robust aggregation pipeline, our method successfully isolates a pure style signal from non-parallel data. Experimental results demonstrate that Causal-Steer enables linear, bidirectional, and compositional control over diverse stylistic attributes such as conceptual complexity and formality, while preserving generation quality and exhibiting strong cross- cross-lingual adaptability. These findings highlight the practicality and effectiveness of our approach, offering a continuous and robust paradigm for adaptive and steerable language models beyond discrete control.
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ACKNOWLEDGMENTS
This research was supported by the National Natural Science Foundation of China (No. 62507040), the Ningbo "Yongjiang Talent Program" Youth Innovation Project (No. 2024A-156-G), and the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (No. 2025C02022).
ETHICS STATEMENT
Our research fully adheres to ethical guidelines for responsible AI and machine learning research. All datasets used in our experiments are publicly available datasets. No proprietary, sensitive, or private data used in this study.
In addition to computational experiments, we conducted a small-scale human study to assess text style. Participation was voluntary, and all participants provided informed consent before taking part. The study was reviewed and approved under our institution's 's internal review procedures, and participants' privacy and confidentiality were fully protected. All collected data anonymized and used exclusively for research purposes.
We acknowledge that responsible research entails careful consideration of potential harms and social impacts. Our work does not involve manipulative, discriminatory, or unsafe practices, and we have designed our experiments minimize any negative consequences participants and the broader community.
REPRODUCIBILITY STATEMENT
We have taken several measures to ensure the reproducibility of our work. First, detailed descrip- tions of our methods, model architectures, and algorithms are provided in the main text, and the corresponding code made available through the anonymous repository ht 4open. wwtp:/ccceennceeeeeeeeeeeeeeeeee 01C1] which is also referenced in the supplementary materials. Sec- ond, all datasets used in our experiments are publicly available and included in the anonymous repository, along with complete data processing steps and instructions necessary replicate our experiments. Third, in the appendix, we provide detailed explanations of the experimental setup, hyperparameters, evaluation protocols, including prompt design and review procedures, to allow accurate replication of our computational and buman-in-the- experiments. Together, these re- sources provide all information necessary for reproducing our results.
REFERENCES
Takuya Akiba, Makoto Shing. Yujin Tang, Qi Sun, and David Ha. Evolutionary optimization of model merging recipes. Nature Machine Intelligence 7(2):195-204, 2022.
Katherine Atwell, Sabit Hassan, and Malihe Alikhani. Appdia: A discourse-awee aware transformer- based style transfer model for offensive social media conversations. arXiv:2209.08207, 2022.
arXiv preprint
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Meri Coleman and Ta Lin Liau. A computer readability formula designed for machine scoring. Journal of Applied Psychology, 60(2):883, 1975.
Jasper Dekoninck, Mare Fischer, Luca Beurer-Kellner, and Martin Vechev. Controlled text genera- tion via language model arithmetic. arXiv preprint arXiv:2311.14479 2023.
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. Eli5: Long form question answering. arXiv preprint 197.09990..09190 2019.
Yujie Feng. Liming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S Yu, and Xiao-Ming Wu. Geoedit: Geometric knowledge editing for large language models. arXiv preprint a5Xiv:2502. 19953, 2025.
11

---

Published as a conference paper at ICLR 2026
Zijian Feng, Hanzhang Zhou, Kezhi Mao, and Zixiao Zhu. FreeCtrl: Constructing control centers with feedforward layers for learning-free free controllable text generation. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 7627-7640) Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10. 0/665///1/022.allllooooo URL
wrgp:/hhoooooooog////gg wtt.:orgg//222c.oo aannnonnclnnng.gooooooooo 412/
Rudolf Flesch. Flesch-kincaid readability test. Retrieved October, 26(3):2007, 2007.
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. Real- toxicityprompts: Evaluating neural toxic degeneration in language models. arXiv preprint arXiv:2009. 1462, 2020.
Google Deep Gemini Team. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint, 2025. Mxii::2007.0621..
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, al. The Ilama 3 herd of models. arXiv preprini arXiv:2407.21788, 2024.
Jingxuan Han, Quan Wang, Zikang Guo, Benfeng Xu, Licheng Zhang, and Zhendong Mao. Dis- entangled learning with synthetic parallel data for text style transfer. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pupers), pp- 15187-15201, 2024.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3 2022.
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. arXiv preprint arXiv:2212.04088, 2022.
Sara Kangaslahti and David Alvarez-Melis. Continuous language model interpolation yields dy- namic and controllable text generation. Transactions on Machine Learning Research, 2025.
Tassilo Klein and Moin Nabi. Contrastive perplexity for controlled generation: application in detoxifying large language models. arXiv preprint urXiv:2401.0849) 2024.
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference- time intervention: Eliciting truthful answers from language model. In A. Oh, T. Nau- mann, A. Globerson, K. Saenko, M. Hardt, and Levine (eds.), Advances in Neural Information Processing Systems, volume 36, pp. 41451-41530. Curran Associates, Inc., 2023. URL httes::/prooeedings... F 21112//83300301222000000000535-3
Xun Liang, Hanyu Wang. Yezhaohui Wang, Shichao Song. Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, et al. Controllable text generation for large language models: A survey. arXiv preprint (tth:2408.//9999 2024.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language pro- cessing. ACM computing surveys, 55(9):1-355 2023.
Oscar Mañas, Pierluca D'Oro, Koustuv Sinha, Adriana Romero-Soriano, Michal Drozdzal, and Aishwarya Agrawal. Controlling multimodal lims via reward-guided decoding. arXiv preprint arXiv:2508. 2025.
G Harry Me Laughlin. Smog grading-a new readability formula. Journal of reading, 12(8):639-646, 1969.
OpenAl. Introducing gpt-4.1 in the api. OpenAl blog, 2025. URL https://openai..e Index/gpt.44 4-1] Accessed: 2025-09-24.
12

---

Published as a conference paper at ICLR 2026
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal. Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.
Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang. Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yugiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2024. Preprint.
Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steering Ilama via contrastive activation addition. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Com- putational Linguistics (Volume 1: Long Papers), pp. 15504-15522, Bangkok. Thailand, August 2024. Association for Computational Linguistics. doi: 10. 1/2/66655/1222222lllllloo httpss:/aclanttoologs wwwp 202.22002...11ommm acl-long. 828/)
URL
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint arXiv:2307.092 07288888..... 2023a.
Hugo Touvron, Louis Martin, Kevin R. Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami. Naman Goyal. Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xia Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint, 2023b. XXXv:2337.00288.
Alexander Turner, Ian Gemp, Andreea-Maria Rusu, James Moulder, George Toderici, Ivan Timo- feev, Brandon O' O'Donoguue, and Cagan Anil. Steering language models with activation engineer- ing. arXiv preprint arXtv:2312.01663, 2023a.
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan Vazquez, Ulisse Mini, and Monte MacDiarmid. Activation addition: Steering language models without optimization, arXiv e-prints, pp. arXiv-2308, 2023b.
Lev Vygotsky. Mind society: The development of higher psychological processes. Harvard university press, 1978.
Qingsong Wang. Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, and Jingyuan Chen. Cogmitive-leve' adaptive generation via capability-awarr retrieval and style adaptation. arXiv preprint arXiv:2509, 25t.:::25000.9966,, 2025. Accepted to Findings of EMNLP 2026.
Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D Man- ning, and Christopher Pouts. Reft: Representation finetuning for language models. 2024. URL
https://axxiv. org/abs/2404.0359..
Hanyu Zhang, Xiting Wang, Chengao Li, Xiang Ao, and Qing He. Controlling large language models through concept activation vectors. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 25851-25859, 2025.
Yi Zhang, Tao Ge, and Xu Sun. Parallel data augmentation for formality style transfer. arXiv preprint XxXiv:2005.07522 2020.
13

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Published as a conference paper at ICLR 2026
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405, 2023.
A STATEMENT ON THE USE OF LLMS
We report the use of large language model in the preparation of this manuscript. Specifically, we used Gemini 2.5 (Gemini Team 2025) to identify and correct grammatical errors and to improve the overall readability of the text. The authors retained full responsibility for all content, and the final version of the paper reflects our own edits and revisions.
B DETAILS OF EXPERIMENTAL CONFIGURATION
To ensure the reproducibility of our results, we provide a detailed description of the experimental configuration used for training, inference, and evaluation. All experiments were conducted on four NVIDIA A100 GPUs with 80GB memory each. Our model was trained for a total of epochs with a per-device batch size of 8, a learning rate of 1 x 10-1, gradient accumulation steps set to 1, and maximum sequence length of 512 tokens. For parameeter-efficient fine-tuming
employed Low-Rank Adaptation (LoRA) with a rank r = 8, scaling factor a 16, and a dropout rate of 0.05. The PEFT library was used to automatically identify and adapt the appropriate linear layers within the model. In addition, a contrastive learning objective was incorporated with the contrastive loss weight À 0.05. For inference, we used deterministic decoding (greedy search without sampling) with a maximum generation length of 512 tokens. During evaluation with GPT-based scoring, the temperature was set to 0.2 ensure stability and consistency of judgments.
C DETAILS OF BASELINES
We benchmark our method against comprehensive suite of baselines for controllable generation, categorized into prompt-based and activation- based approaches. For our primary task of conceptual complexity control, we include zero-shot instructional prompting and three prominent activation steering methods: Representation Engineering (RepE) (Zou et al. 2023), Contrastive Activation Ad- dition (CAA) (Turner et al.) 2023a). and Inference- Time Intervention (TTI) (Li al., 2023). Distinct from these activation- based techniques, we also include Continuous Language Model Interpola- (CLI(CLMI) (Kangaslahti & Alvarez-Melis 2025) for our main experiment, which achieves control by interpolating the weights of Low-Rank Adapters (LoRA) rather than steering activations. For the activation-based methods, we evaluate both the standard approach using the final token's hid- den state and stronger variant we implement that averages hidden states across all tokens (-avg). For the toxicity detoxification task, we include additional specialized baselines such as the prompt- based POSPROMPT and activation steering methods including Arithmetic (Dekoninck al. 2023 ActAdd (Turner et al. 2023b)) and GCAV-Output (Zhang et al. 2025).
D DETAILS OF DATASETS
We evaluate our method on primary task of controlling conceptual complexity and assess its gen- eralizability on two additional stylistic dimensions: toxicity detoxification and formality control.
For our primary task, conceptual complexity, we extract the corresponding style vector from the Scald] question-answering corpus with graded difficulty levels. We then evaluate the model's gl
ability to modulate content difficulty on a test set 100 questions randomly sampled from the EL15 dataset (Fan et al. 2019).
To demonstrate the versatility of our method, we conduct two generalization experiments. For tox- icity control, we derive the style vector from the APPDIA dataset (Atwell et al. (022). which pro- vides aligned toxic and detoxified sentence pairs. We evaluate its performance following the setup of Zhang al. (2025) on two subsets of the CealToxicityProppe corpus (Gehman et al. 2020
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one with highly toxic prompts and another with randomly sampled ones. For formality control, extract the style vector from 16,000 sentence pairs randomly sampled from the parallel corpus of Zhang al. 2020).
Since both the APPDIA and formality datasets are originally composed of parallel sentences rather than question answerr pairs, we adapt them to the istruction-followiing setting: we GPT-4.1 to generate corresponding questions & for each pair and slightly modify the answers to better align with instruct-style training. The exact prompting procedure is detailed in Appendix G.I and the adapted datasets are provided both in the appendix and via our anonymous repository.
E HUMAN EVALUATION
To better assess the relative performance of our method, we conducted human evaluation us- ing a Best-Worss Scaling methodology. This approach reduces annotator bias compared to direct scoring. For each of the 50 test ques- tions, annotators were shown the Simple and Complex outputs from our method, CAAmean- and CLMI in randomized, blind setting. For each set of three, they were asked to select the single "Best" and "Worst" response based on holistic judgment of style appropriateness and overall quality (e.g.. fluency, coherence).
Table 6: Human evaluation results based on Best- Worst ranking. We report the percentage of times each method was chosen as Best, Middle, or Worst by 3 annotators across 50 questions. A higher 'Best' percentage indicates stronger pref- erence.
Complex Style (%)
Simple Style (%)
Method
Best
Middle
Worst
Best
Middle
Worst
CLMI
11.3
25.3
63.3
14.7
28.0
57.3
CAAmen
23.3
41.3
35.3
26.0
45.3
28.7
59.3
The results, summarized in Table 6 show strong preference for our method. For the Com- plex style, our model was chosen "Best" in decisive 65.3% % of cases, significantly outperforming
CAAmean (23.3% and CLMI (11.35 (11.3%).
A similar trend observed for the Simple style, where method again preferred "Best" in 59.3% of evaluations. This confirms its robust bidirectional control. The low percentage of our method being selected as "Worst" (1.3% for Complex and 14.0% for Simple) further underscores its stability and reliability. In contrast, CLMI was rated "Worst" in the majority of cases (63.3% for Complex), highlighting its limitations achieving effective style control. These findings from our comparative human evaluation strongly validate the superiority of our proposed framework.
F COMPUTATIONAL OVERHEAD
The primary computational cost of our framework is in- curred during the offline style vector extraction phase. In contrast, the overhead during online inference is negligi- ble. As shown in Table 7 the generation speed, measured in tokens per millisecond, remains nearly identical to the baseline even when applying multiple steering vectors si- multaneously. The control intensity (a) has no impact this speed, as the underlying operation is a simple vector addition. This efficient design, which concentrates putational effort into a one-time offline process, makes method highly practical for real-tima applications.
Table 7: Inference speed in tokens per millisecond (tokens/ms). Higher val- ues are better. Our method maintains speed comparable to the baseline.
Configuration
Speed (tokens/ms)
Baseline
1.17
vector, 20
I.13
vector, 30
1.16
vectors, a = 20
1.12
2 vectors. = 33
1.04
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G PROMPTS
G.1 PROMPTS FOR DATASETS REWRITE
Transform the given pair of parallel sentences (e.g., formalinformal, toxicdetoxifiee) into unified QA
format.
Steps
1. Generate one natural and coherent question (Q) that could reasonably elicit both given answers.
2.Produce two answers (Al and A2) corresponding to the styles in the original parallel data:
the pair is formal vs. informal, ensure that A is informal and A2 is formal. the pair toxic detoxified, ensure that Al retains the toxic style while A2 provides the detoxified version.
3. You may expand the answers with additional content increase richness, but keep both answers semantically consistent with each other. Always preserve intended style (informal vs. formal; toxic vs. detoxified).
G.2 PROMPTS FOR CONCEPTUAL COMPLEXITY TASK
You are expert evaluator for natural language generation quality.
Evaluate the following text based on four dimensions. Focus especially on conceptual complexity.
1. Relevance: Does the response stay topic and dircctly address the question topic without irrelevant content?
2. Fluency: the language clear, coherent, and easy read. with proper grammar and logical flow?
3. Scientific Accuracy: Are the knowledge points and explanations factually correct and reliable based on authoritative sources?
4. Knowledge Difficulty: Assess whether the knowledge level matches the expected difficulty.
Higher Highly technical research-level content suitable for graduate-level understanding.
Lower scores: Simple, introductory, or popular-science level explanations. Give each dimension score from (poor) 10 (excellent).
G.3 PROMPTS FOR FORMALITY TASK
You are expert evaluator for natural language generation quality.
Evaluate following text based four dimensions. Focus especially on formality.
1. Relevance: Does the response stay topic and directly address the question topic without irrelevant content?
2. Fluency: the language clear, coherent, and easy read, with proper grammar and logical flow?
3. Tone Appropriateness: Is the tone of the response (e.g., professional, casual, empathetic, neutral) suitable for the prompt's 's context and implied audience?
4. Formality: Assess the text's level of formality based on word choice, sentence structure, and adherence
stylistic conventions, (Assign score 5 for standard model output.)
Higher scores: Highly formal language, sophisticated vocabulary, complex sentence structures, and an objective tone. Avoids slang, contractions, and colloquialisms.
Lower scores: Highly informal conversational language. May use slang, contractions. simple sentence structures, a personal or subjective tone. Give each dimension score from (poor) to 10 (excellent).
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Published as a conference paper at ICLR 2026
H CASE OF CONCEPTUAL COMPLEXITY TASK
Question: Do magnetic and/or electric fields have any influence on time and space?
10 (Simple)
Yes. both the electric and magnetic fields can affect how measure time and distance in certain situations especially when dealing with very fast speeds close to light or strong gravitational fields.
le Le Fieldæe; In general relativity, electric charge at rest creates no direct effect spacetime that would change our usual way of measuring distances
times (like using clocks). However, if you consider charged partieles moving through an electromagnetic field, it's important to remember that they experience forces from those fields which could make their paths twist around as seen from other frames of reference. But this doesn't directly mean there is change in the measurement of time and space for them, rather, it means that things get more complicated because now you need to take into account just gravity but also these extra forces coming from the electric charges.
2. 2.* MaggMtttt Field=:; Similar to the case with electric fields, a single magnet sitting still wont cause changes in measurements of time and space unless something else interacts with its field. When considering objects moving near each other under the influence of magnets, like two wires carrying current, then yes, the movement of wire affects another due the changing magnetic field, making everything move differently than expected without taking the magnetic force into consideration. This makes the path taken look different depending where you't re standing kind of like seeing someone walk diagonally instead of straight ahead while riding merry-go-round! S+aaccttme Curvature Due To EM Energy+:; Just mass curves spacetime according General Relativity, energy carried by photons (light) does so too. So having lots of electricity and magnetism swirling about will curve spacetime slightly, much like putting heavy ball down onto a trampoline causes it to bend downwards. It might be tiny bending compared to planets and stars, but exists! In summary, while individual static electric and magnetic fields don't usually warp time and space themselves, once you start talking about moving charges.
= -5 (Medium)
Yes, both the electromagnetic fields (electric and magnetic) can indeed affect spacetime according to modern physics theorics, particularly general relativity.
In Albert Einstein's 's theory General Relativity, gravity is described as curvature spacetime caused by mass and energy. This means that not only does massive matter curve spacetime but also strong or dynamic electromagnetic fields can contribute this curvature under certain conditions. Specifically: 1. +* - Maxwell Equations=s; These equations combine Maxwells equations for electromagnetism with Einsteins field equations from general relativity. They describe how an electromagnetic field affects the geometry of spacetime through its stress-energs tensor term.
2. GGravitomaggettic Effects**; extreme scenarios involving very high currents or intense magnetic fields, there could be significant gravitomagnetic effects predicted by these unified models. However, such effects would typically require extremely large-scale phenomena beyond what we encounter everyday situations.
3. ** Quantum Gravity Theoricss:: At quantum scales, where gravitational forces become comparable to other fundamental interactions like those mediated by the clectromagnetic force, more speculative thcoretical frameworks may come into play. Some proposals within loop quantum gravity or string theory suggest that even at microscopic levels, electromagnetic fields might influence spacetime structure indirectly via their contributions to the overall quantum state of spacetime. Experimental Observations+v: While direct experimental evidence supporting these predictions remains limited due to technological constraints, indirect observations and precision measurements continue to refine our understanding of how strongly coupled systems behave near black holes or neutron starsenvironmens characterized by highly relativistic and possibly magnetized conditions. It's s important to note that while theoretically possible, the practical implications of electromagnetic influences spacetime remain largely confined to astrophysical contexts rather than macroscopic terrestrial environments. Nonetheless, ongoing research continues to explore these fascinating intersections between electrodynamics and gravitational physics.
17

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Published as a conference paper at ICLR 2026
20 (Complex)
Yes, hoth electromagnetic fields (which encompass the electric and magnetic fields) can indeed exert influences on spacetime within certain thcoretical frameworks of modern physics.
### Electromagnetic Fields in General Relativity
In context general relativity, formulated by Albert Einstein, gravitational effects are described through the curvature of spacetime. However, this framework primarily accounts for mass mass-energy contributions to such curvature. Recent interpretations and extensions these theories incorporate electrodynamics into considerations of spacetime structure:
1. ** Quantum Gravity Theoriesss: These speculative frameworks attempl reconcile quantum mechanics with general relativity. In some models, quantized electromagnetic interactions could contribute non -trivially to spacetime metrics at microscopic scales.
2. #*Non--inees Linear Electrodynamicsee: Certain modifications nonlinear formulations of classical electromagnetism predict deviations from Maxwellian predictions under extreme conditions relevant high-field regimes near black holes compact astrophysical objects.
### Quantum Electrodynamic Effects
At the microscale, phenomena governed by quantum electrodynamics (QED), which extension of classical electrodynamics incorporating principles of quantum mechanics, exhibit significant implications:
1. #*Casimir Effecte:: This phenomenon arises due to quantum fluctuations in vacuum states between two closely spaced conductive surfaces. It involves spatial configurations influenced by electromagnetic boundary conditions, manifesting as measurable physical forces.
Vacuum Polarization* =: Within QED, virtual particle pairs (electrons positrons) transiently interact with intense electromagnetic fields, leading to observable macroscopic consequences via renormalization parameters.
### Lorentz Violations and Anisotropic Spacetimes
Theoretical explorations often invoke violations of local Lorentz symmetryspatial isotropy and temporal homogeneity postulated foundational tenets of special relativistic formalismsto model emergent phenomenologica descriptions consistent with experimental data across various energy scales.
I
CASE OF FORMALITY TASK
Question: why do most bipedal robots always keep their knees bent a bit when standing?
25 (Informal)
Most bipedal robots keep their knees slightly bent for several reasons:
1. Stability::: Keeping the legs in slight bend helps with balance and stability. When the robot is standing still or walking, this position makes it easier to stay upright without falling over.
2. >*Energy Efficiency+4: A little bend in the knee help energy because its hard the
motors that move the leg parts (like servos). ss like how you might bend your knees before jumping uses less power hut gives more push-off force.
3. #hohcck Absorptionss: Just like humans, hending at the knees can act as a shock absorber during steps jumps. This
the robot doesnt have work hard handle bumps uneven ground. 4. #*Flexibility & Range of Motion=::; Bending the knees keeps them from getting stuck straight up or down all the time. This lets the robot be more flexible about where its feet go next, which important if it needs to walk around stuff change directions fast.
*eLess Stress On Joints Bones** real life. keeping joints loose instead locked up stops things from breaking under pressure. Same for these bots they don't It want metal bones snapping!
So basically, just like we use our bodies' bends to feel comfy and strong while standing. robots get way into those same tricks too!
18

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Published as a conference paper at ICLR 2026
-10
Most bipedal robots maintain a slight hend in their knees while standing for several reasons:
1. *+ Stabilityy::; Keeping the legs at an angle (bent) helps distribute weight more evenly and provides better stability against tipping over. This similar how humans stand with slightly bent knees.
2. w*Energy Efficiency+:: A small amount of knee flexion can help store potential energy elastic strain the leg muscles or tendons. When walking or running, this stored energy can be released assist in propelling the robot forward, making it more efficient.
++Shock Absorptiones; The slight bend acts like shock absorber during movement. It reduces impact forces both joints and other parts of the body, which could otherwise cause damage from repeated hard landings.
4. **Joint Health=#; Maintaining some degree of joint flexibility prevents stiffness that might occur if they were locked straight all the time. Stiffness increases wear and tear on mechanical components.
5. Control Precision=+: robotics, keeping certain angles allows engineers to fine-tune control
algorithms because there's less chance of hitting physical limits compared to having fully extended
limbs where you'se d hit "hard stops." "
6. Humanoidd Design Consistency#s: For humanoid robots designed to mimic human movements,
maintaining natural stance makes them look more lifelike and casier interact with since people expect others to have naturally curved postures rather than rigidly upright ones.
Overall, these factors contribute to improving performance, longevity, and user interaction for bipedal robots!
10
Most bipedal robots maintain a slight bend in the knee for several reasons related to stability and efficiency
Stabillity::: A slightly bent posture helps improve balance and stability. This is similar to how humans naturally stand with some degree of flexion at the knees. The bending allows the robot's center mass to be more centrally positioned over base, reducing the risk tipping or falling,
2. ++Energy Efficiency+:: Bent knees can help reduce energy consumption during static stance phases. By mechanical systems (such as motors) require less effort support the weight of the body without additional strain joints that might occur if they were fully extended.
maintaining this position,
#*Dynamic Stability During Movementes: When performing dynamic movements such as walking or running, having flexible joint angles like those found in human anatomy enables better control and adaptability. Bipedal robots often use these principles to enhance their ability to navigate various terrains and respond external forces dynamically.
4. Biologicca Inspiration Many robotic designs draw inspiration from biological models, including human locomotion pattems. Mimicking natural postures can lead movement strategies.
effective and efficient
5. Reduction of Stress Joints and Components Keeping certain joints moderately flexed reduces stress on both the physical components within the robot and potential wear and tear associated with prolonged periods of full extension.
These factors collectively contribute why many bipedal robots adopt posture involving mild knee bends while standing moving. However, specific design choices may vary based on the intended application, capabilities, and goals of each individual robot model.
19

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Published as a conference paper at ICLR 2026
20 (Formal)
Most bipedal robots maintain a slight hend in the knee for several reasons:
1. ++ stabillity and Balancess; Bending the knees slightly helps to enhance stability and balance. This posture allows robot to distribute weight more effectively across the lower limbs, reducing the risk of tipping imbalance.
Possuraal Adaptation+:: Human beings naturally adopt such postures to biomechanical advantages that facilitate efficiint movement and support various activities. Similarly, robotic designs often mimic human postures improve functionality and adaptability.
3. ** Flexibility and Dexterity=:; A slight bend in the knees provides greater flexibility and dexterity, enabling the robot to perform wider range of actions with ease. facilitates movements such as walking, climbing. and manipulation tasks.
4. #Energy Efficiency+s; Bent knees can contribute to energy efficiency during locomotion by allowing smoother transitions between different phases of gait (such stance phase and swing phase).
5. ++Saetty Considerationss Maintaining an upright yet flexible posture reduces stress on joints and musculature over prolonged periods, which is crucial for long-term operation without fatigue damage.
Mimicrr of Natural Movementses; Many robotic systems aim emulate natural human behaviors better integrate them into environments where they interact closcly with humans, thereby enhancing social acceptability and effectiveness.
These factors collectively explain why many bipedal robots employ posture characterized by mild flexion at the knees while maintaining other aspects similar to those observed in human physiology.
20

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Published as a conference paper at ICLR 2026
J CASE OF CROSS-LANGUAGE GENERALIZATION
To further evaluate the cross-lingual capability of our model, we tested whether activation vectors extracted from English datasets could control formality in German -antiionnnneiiing tasks. The experimental results, presented below, demonstrate that activation vectors effectively generalize across languages, maintaining robust control over stylistic attributes in the target language.
Question:Die globale Finanzkrise von 2007/2008. Wird eine weitere geben?
a = 20 (Informal)
Es ist schwierig sagen, und wie das nchste groe Finanzspielzeug wird, aher kann sicher sein, dass es immer noch Risiken gibt, die dazu fhren knnen, dass der Markt pltzlich Schwierigkeiten
gert.
Hier sind cinige Gmdd, warum ein neuer Finanzschlafmite vielleicht kommen knnte: Hilfsschlaffmtzenee: Wenn jemand versucht, den Markt mit Hilfe von Geld aus dem Nichts machen will sozusagen einen groen Spielball fr sich selbst dann kann das zum nchsten groen
Schlafitzznsppill werden.
Beispiel: Wie bei den Hauseinlagen, die vor vielen Jahren als super-sicher gedacht wurden, aber im Endeffekt viele Leute tiefen Schlfer aufwachen lieen, wenn ihre Hausgeldkarten nicht gut genug versteckt hielten oder vergaen, dass sie gar kein echtes Geld waren!
**Zu viel Geld suchen nach einem Ort, um zu spielenes:
Wenn alle Banken und andere Menschen, die Geld haben wollen, keine gute Idce finden, sie ihr Geld
lassen sollen, dann knnten sie versuchen, etwas ganz Neues zu finden!*
3. s*Menschen. die glauben, dass alles okay ist**, wenn nicht wirklich okay ist:
Wie wenn du weit. dass dein Auto kaputt ist, aber du fhrst trotzdem damit, weil du denkt, dass es schon
lange keinen Fehler gemacht hat.
4. ** Ganze Lnder, die dummspiclene#:
Was passiert,
alle Leute einer Stadt oder Land, das sehr viel Geld hat, einfach Spa hahen wollen, ohne richtig darber nachdenken, was genau sie tun? Es wre wie wenn alle Freunde in deinem Kindergarten zusammenhelfen wrden, um ein riesiges Sandburg-Haud zu bauen, aber niemandem kommt in den Kopf, dass es Zeit ist, mal rber zur Wasserbrumen-Schaukk zu gehen!*
20 (Formal)
Es ist schwierig sagen, ob und wann genau eine neue Finanzkrise passieren wird, aber wir knnen sicherstellen, dass das Risiko immer ist. Hier sind einige Punkte zum Thema Finanzrisiken: Lernende Systemess; Nach der groen Krise 2007/2008 haben viele Lnder und Banken versucht, ihre Systeme besser zu machen. Das bedeutet nicht, dass sie perfekt sind oder nie wieder Probleme haben werden.
1,
2. DeDebtBBubbles: Eine groe Schuldeabombe kann sich bauen, wie in den Jahren vor dem Crash. Wenn Leute, Unternehmen oder Regierungen viel Geld leihen, ohne genug Kredite zurckzahlen knmen, kann das zu einem Problem fhren.
ein greer Teil des Problems im Jahr 3. 2007/2008. Wenn der Immobilienmarkt pitzlich abfllt, kann das einen Sturz starten.
Immohilicablschreei
#TTchhooloieffimmnee: Heutzutage gibt es andere Sektoren, die riesige Betten voller Geld haben knnten z.B. Tech-Firmen mit hohen Bewertungen. Ein Schlag fr diese Firmen knnte alles verndern. Klimmwwandelo:: Die Vernderte Weltwirtschaft durch den Klimawandel kann auch neuen von
Krisen fhren, die uns berraschen knnten.
Poliiii und Rechtswesenee: Politische Unruhe Schwierigkeiten der Steuerung von Marktsystemen knnen auch dazu fhren, dass Dinge aus dem Ruder laufen.
Um solche Krisen zu verhindern oder wenigstens zu dmpfen, mssen I Inder und Institutionen stndig aufpassen und sich anpassen. Es hilft auch, wenn jeder seine eigene finanzielle Situation gut kontrolliert und keine zu vielen Risiken nimmt. Insgesamt ist es wichtig, dass alle Beteiligten vorsichtig bleiben und immer nach besseren Mglichkeiten suchen, Risiken minimieren.
21

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Published as a conference paper at ICLR 2026
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Figure 6: Heatmap of average style control scores across layer blocks. The y-axis and x-axis represent the start and end indices for the intervention block, respectively. Scores are averaged over multiple runs.
(a) Knowledge Difficulty Score
(b) Formality Score
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Figure 7: Two-Dimensional Stylistic Control. Model output scores for (a) Knowledge Difficulty
and (b) Formality under simultaneous steering.
22


Reviews:
[{"review_id": "aVryM1mOPG", "reviewer": "Reviewer_1XAb", "summary": "In this paper the authors present Casual-Steer, an approach to control LLM output with a single linear scale to move between two reference styles. The approach makes use of LoRA and a specialized loss to allow for controllable weights between the two styles. The authors investigate a number of approaches for extracting a controllable vector and primarily make use of a PCA-based approach. The authors compare to a number of text stylization baselines, finding similar performance across many text quality metrics, all of which arise from prompting ChatGPT-4.1.", "questions": "1. What were the standard deviation values for the metrics in the paper?\n2. Why did the authors focus on text quality?\n3. In Table 4, why does Casual-Steer present the lowest toxicity in both cases?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "The originality of the paper is quite high. While LoRA controllability approaches exist, none make use of this specific setup. If successful and generalizable this approach could be very useful for stylized text generation without expensive additional processing. This is the greatest strength of the paper. \n\nThe quality of the work in terms of the system overview is high. The experiments are more mixed. The use of two different models (even if they are somewhat small) and three different pairs of datasets is excellent. However, the reliance on ChatGPT-4.1 for all metrics is not ideal. Further, the authors do not present standard deviation or other analyses of the distribution of scores. \n\nThe clarity of the paper is also high. The authors' approach is well-explained and outside a few grammar issues the paper is well-written. The figures are also clearly presented. \n\nThe significance of the work is hampered primarily due to the issues presented with the results.", "weaknesses": "I think this is overall a strong paper hampered by weak evaluations. The authors primarily make use of ChatGPT-4.1 for evaluating the output text and primarily make use of text quality metrics. Table 1 demonstrates very little difference in ChatGPT-4.1 scores. The authors do not present any standard deviation values, making it unclear if they ran each experiment a single time (not ideal) or if the std dev values would make clear that all outputs fit within the same distribution. The Flesch grade level is the most useful metric in the initial table, but even this shows very minor variation in most cases. There is no standard deviation reported for this either. \n\nSimilarly, the Toxicity metric results in Table 2 are somewhat confusing. Assumably the Toxic style should be more toxic, not less toxic, but the authors' approach achieves the lowest toxicity in both cases. Unfortunately no standard deviation score is reported.\n\nThe human evaluation, while it only appears in the appendices, is too small to draw any generalized takeaways from, with only three annotators. The annotators also only evaluate the quality of the text. \n\nGiven the authors claims around controllability rather than text quality I would have preferred to see evaluations around the controllability of the text. \n\nMore broadly, only a single text example is given in the paper, including in the appendices. \n\nGiven all of this, the results are simply not sufficient to determine the extent to which the authors' claims are supported.\n\nThere are also a few grammar issues in the paper. For example in 4.0 it should likely be \"Evaluation Metrics\" not \"Evaluation Metric\" and \"in the Appendix E\" should just be \"in Appendix E\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RlkDk7RD55", "reviewer": "Reviewer_m2bL", "summary": "This paper introduces a novel framework to improve activation steering for stylistic attributes, by reframing LoRA as a causal intervention tool. The authors claim that by using LoRA, they are able to disentangle stylistic and semantic variation, thereby allowing for better steering on tasks that no longer rely on parallel data. Additionally, the authors add PCA based denoising to identify vectors that are transferable across different task settings. Concisely:\n1. The authors use LoRA to introduce a low rank perturbation that can extract style-specific activation differences.\n2. Extracted style vectors are denoised using PCA\n3. Style vector is normalized and used for activation steering.\nAuthors test for generalization across multiple task settings and models.", "questions": "1. What is the difference between parallel vs contrastive corpora? \n2. Why is this a \"causal\" steer? The ablation studies show that all factors are important., so which is the causal factor here? Why is LoRA training alone attributed causality? \n3. \n>However, this method [difference-in-means] assumes that content-related information can be eliminated via vector subtraction, thereby isolating the pure style signal.\n\nDon't the authors make this same assumption but for the LoRA fine-tuned activations? Why would the former not hold while their difference holds?\n4. It's unclear how the layers where the steering vectors were applied were selected, or how many layers the intervention is applied on. The authors simply mention \"Specifically, for a selected set of layers $L_{steer}$, we intervene in the output of the MLP submodule during the generation of each token $t$\". How many layers are being steered on? How are these layers determined? Is the intervention applied on the entire MLP output or on a partial subset -- it's unclear.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Strong empirical work, with extensive experimentation across multiple models and tasks, thus showing evidence for generalization.\n- Comparisons with multiple baselines.\n- Causal-Steer is useful at higher steering factors alpha\n- Fun multi-lingual example testing, though in appendix.", "weaknesses": "I am really confused by the core claim this paper is making. My understanding of the claim is that Causal Steer\n- Eliminates the need for parallel corpora, but needs contrastive corpora\n- Separates stylistic differences while keeping content the same\n\n1. What is the difference between parallel vs contrastive corpora? For eg. I suggest the authors look at [1] where the authors steer using two datasets that are not parallel (as in are not required to differ at 1 token position etc) and instead just exhibit contrastive meta properties, i.e. one dataset contains harmful questions and the other contains helpful questions. They use difference in means activation and it works very well, even though there isn't a high consistency between the contrastive inputs.\n\n> However, this method assumes that content-related information can be eliminated via vector subtraction, thereby isolating the pure style signal. This assumption holds only under the condition of extremely high content consistency between texts, which in turn necessitates a meticulously crafted parallel corpus. Causal-Steer, in contrast, captures the LoRA-induced perturbation on the very same input di, elegantly circumventing this reliance on parallel corpora.\n\nthen becomes unsubstantiated.\n\n2. The paper should also look at ReFT ([2]), which uses LoRA adaptors to fine-tune the representations model directly on a set of inputs and outputs, therefore truly no parallel corpora and is highly performant and efficient. This should definitely be a baseline in your paper\n\n3. It's unclear how the layers where the steering vectors were applied were selected. The authors simply mention \"Specifically, for a selected set of layers $L_{steer}$, we intervene in the output of the MLP submodule during the generation of each token $t$\". How many layers are being steered on? How are these layers determined? Is the intervention applied on the entire MLP output or on a partial subset -- it's unclear. \n\n4. The authors utilize the same activation difference approach used in earlier methods, but with LoRA, and by doing so they negate some of their own claims such as \n\n>However, this method [difference-in-means] assumes that content-related information can be eliminated via vector subtraction, thereby isolating the pure style signal.\n\nDon't the authors make this same assumption but for the LoRA fine-tuned activations? Why would the former not hold while their difference holds?\n\n5. The use of causal-intervention is very shaky here. The intervention is LoRA training + PCA over _all_ layers + geometric mean. Any of these could be causing the behaviors produced by the authors. The ablation studies show that all factors are important.\n\n6. Unclear what metrics are being reported in each table. The information in the appendix is valuable. Could the authors update the table captions as well as the main text to reflect that the scores are from 1 - 10?\n\n7. Missing citations. A few hand-picked examples:\n\n> We attribute this failure to its use of PCA for analyzing activations, a technique that requires a much larger dataset to identify a meaningful style direction. This hypothesis is supported by our generalization experiments, where its performance improves on the larger Formality dataset (16,000 examples). \n\n> Intervening in the initial layers (1-10) proves far less effective and can even be detrimental to the output. This empirical result aligns with the prevailing hypothesis that later transformer layers encode more abstract semantic and stylistic information.\n\n> Flesch Grade Level\n\n8. The text in the figures is extremely tiny in many cases. Please fix this. \n\n9. Many sentences are unclear:\n\n> Removing the contrastive learning objective (“- w/o Contrast”) causes a catastrophic failure in\nbidirectional control, with the model unable to generate ”Easy” content.\n\nWhat is easy content? Why is this \"catastrophic\" failure? Please keep terminology and wording consistent between paragraphs.\n\n10. The main figure is very hard to read, unclear what the different suffixes are given the content at this stage. The formality/informality task comes much later.\n\n[1] Arditi, Andy, et al. \"Refusal in language models is mediated by a single direction.\" Advances in Neural Information Processing Systems 37 (2024): 136037-136083.\n[2] - Wu, Zhengxuan, et al. \"Reft: Representation finetuning for language models.\" Advances in Neural Information Processing Systems 37 (2024): 63908-63962.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NBT2jxGMcz", "reviewer": "Reviewer_S8RX", "summary": "The paper introduces a framework for continuous and bidirectional style control in LLMs without requiring parallel corpora. The key idea is to reinterpret LoRA as a causal intervention. Style-specific LoRA modules are trained in a contrastive learning manner to capture style features. Then, their influence is isolated by comparing activations with the frozen base model. The resulting style vectors after postprocessing are applied at inference time through activation steering. Experiments on multiple benchmarks show that their method outperforms prior methods and achieves controllable style transfer while preserving fluency and relevance.", "questions": "- How can you extend the model to support more general, multi-directional controllable style transfer?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- I like the reinterpretation of LoRA as a causal intervention, which is conceptually novel and technically lightweight.\n- The empirical results are strong, demonstrating the effectiveness of the proposed method.", "weaknesses": "- The method requires two LORA models, which limits the style to be binary.\n- Although I understand that many early style transfer works relied on parallel corpora, I do not consider this to be a novel contribution for LLMs. Using non-parallel corpora can provide the model with richer data for training, increasing the chance that LLMs encounter more diverse lexical distributions.\n- The recently proposed Persona Vectors[1] method from Anthropic is conceptually similar to this work, as both control generation through directions in the latter activations. Several of the other baselines mentioned in the paper are also training-free, in contrast to the proposed approach, which involves additional training and thus carries greater complexity.\n\n[1] https://arxiv.org/pdf/2507.21509", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)