DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
Authors: Xinyu Ma
Yifeng Xu
Yang Lin
Tianlong Wang
Xu Chu
Xin Gao
Junfeng Zhao
Yasha Wang
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Models
Stylized Question-Answering
Representation Editing
Primary area: foundation or frontier models, including LLMs

Abstract: We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model's representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Codes and benchmark datasets are available at https://github.com/ArthurLeoM/DRESS-LLM.

Published as a conference paper at ICLR 2025
DRESSING UP LLM: EFFICIENT STYLIZED QUESTION- ANSWERING VIA STYLE SUBSPACE EDITING
Xinyu Ma', Yifeng Xu', Yang Lin', Tianlong Wang?, Xu Chu 1..3, Xin Gao',
Junfeng Zhao', Yasha Wang 133
School of Computer Science, Peking University
2 Center Frontiers of Computing Studies, Peking University
3 National Research and Engineering Center of Software Engineering, Peking University {max mexxiny nyu, mangyashaa@sa edu. on
ABSTRACT
We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs disentangle style-relevant subspace within the model's representation space to conduct representation editing, ensuring minimal impact on the original semantics. By applying adaptive editing strengths, dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents.
INTRODUCTION
Large language models (LLMs) like GPT-4 (Achiam et al., 2023) and LLaMA-3 (Dubey al., 2024) have demonstrated exceptional performance across a range of natural language processing (NLP) tasks including question-answering This evokes the wide use of LLMS as conversational agents (Weizenbaum) 1966) for various appli- cations, including psychological counseling (Li let al. 2023a),, creating gaming NPCs (non-player characters) (Cox & Ooil 2023) and character sim- ulacra (Shao al. 2023). While LLMs are adept at providing accurate and coherent answers, they lack the intrinsic ability to tailor responses in a specific language style. Language style (Jin et al.) 2022) is linguistically defined as the manner expressing the semantics, depicted by multiple at- tributes like personality, emotion, authorship, era background, etc. Stylized responses are crucial for LLM agents the style can shape the interaction tone, making the agents more immersive and engaging, and ensuring that responses are empathetic and appropriately tailored to the user's emotional states. Hence, crafting the language style is essential for shaping the specific image and personality of conversational agents. Therefore, we aim to solve the following question: How make LLMs respond to user questions in a specificc style?
Pretrained LLM
Can you romind of
dendline for finishin the report?
MHA Layer 1
Steering Vectors of Shakespeare-sttyl
MHA Layer Ag
MHA Layer k
MHA Layer
Stylized Response
Cartes, what be the hour which
MHA Layer N
thou dost requere the report?
Sume what's the doadiine for the report?
Original Response
Figure 1: An illustrative example of representa- tion editing for Shakespeare-ste responses.
Currently, there are two main approaches to achieving stylized responses prompting with few-shot demonstrations and fine-tuning. Prompting methods (Park al. 2023) leverage the in-context
*Coresponding Author
Codes and benchmark datasets
at http::////a.toooooo/////ttrrr//s L.M

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learning ability (Brown et al., 2020) of LLMs by using a description of the target style along with few-shot examples to generate stylized responses. However, simply prompting LLMs is no longer proper instructions are plain and insufficient to describe certain style comprehensively, and demonstrations could severely increase the sequence length, increasing the risk of
(Liu et al., 2024). A better way is to conduct supervised fine-tuning (SFT) (Ma et al.) 2024) with larget style response data (Shao et al. (023), where LLM's outputs are adapted to the target style distribution by adjusting the model parameters. Yet this approach is overly burdensome, particularly for scenarios like game NPC construction. Each character requires a separate fine-tuning process, making the creation of multiple characters extremely costly terms of time and computational resources. Therefore, it is necessary to develop an effective and efficient strategy to reach our goal.
Representation editing (Burns et al. 2023 Turner et al., 2023) has recently been widely used to control specific behaviors of LLMS (e.g., truthfulness enhancement (Zou et al. (023),, knowledge editing (Hernandez et al.) 2023), etc.). Since it operates solely on the representation space without optimizing the parameter space, it is lightweight, train-free, and highly efficient. Additionally, it leverages large amounts of data to compute generalizable steering vectors for depicting specific model functions, making it highly effective. Building this insight, our approach attempts to utilize representation editing methods to craft the style of LLM output. Specifically, as shown in Figure [] aim to solve steering vector that is added to LLM's activations during inference, shifting the representations to the direction of another language style (e.g.. poetic and rhythmic Shakespearean early modern English). This approach fulfills our need to combine the efficiency of train-free method with the effectiveness of data-driven steering for stylizing LLM responses.
However, when building stylized conversational agents, it is also crucial ensure the response quality alongside stylization. In other words, generating stylized responses must not compromise the original semantics. This presents significant technical challenge for our representation editing approach: How to solve a steering vector minimizing the inftuence on the underlying semantics? Recent research observes that in the extremely wide and high-dimensional space of over over-parameterized LLMs, activations can be assumed to be approximately orthogonal with high probability (Wang & Zhu 2023). This implics that the different language functions are likely to reside in orthogonal and disentangled lincar subspaces (Ortiz-Jimena et al. 2023). Hence, insight is to identify style- relevant and semantic-isolated subspace from the representation space to edit within. Building on this insight, we propose DRESS (Disentangling Representation Editing in Style Subspace), comprising the following strategies to progressively locate the style subspaces and perform comanntccissllate style steering. 1) Attention head filtering: It has been demonstrated that different attention heads tend to perform varying functions (Ge et al. 2024). Hence we use probing techniques to identify the attention heads that are more closely related to styles and edit within those heads. 2) Style subspace filtering: To further eliminate the style-irelevvat components in the selected attention heads, we conduct subspace filtering by seeking a subspace supported by style-related bases, so that the impact on semantics could be minimized. 3) Adaptive editing strength: We employ adaptive editing strength on each subspace basis and each generated token to provide higher flexibility and avoid excessively intense editions that could harm the semantics. Compared to previous methods (Zou et al., 2023] Li et al.] (0235)) relying on a single steering vector for editing, our approach offers greater flexibility and expressiveness by introducing higher-rank subspace to represent style. Meanwhile, it filters out style-irrelevant noises within the steering vector, allowing for better semantic preservation.
To validate the effeectiveness of our approach, we construct evaluation benchmark comprising two specific stylized
datasets of different languages (i.e., www.esheare-sttteeeeee in English and Dream of the Red Chamber-style in Chinese The objective evaluation metrics include style intensity, semantic preservation, and fluency, following traditional criteria (Jin et al. 2022 Section 3). Additionally, we utilize the GPT-4 rating as a surrogate for human evaluation (Zheng al. 2023). serving as an overall assessment metric to comprchensively evaluate the model 's capabilities.
To summarize, we highlight our contributions as follows. We proposed a lightweight and train-free representation editing method dubbed DRESS based on the decoupling of language style subspaces to enable stylized LLM QA systems, which lays a fundamental groundwork for constructing humanoid conversational agents. Technically, we propose three mechanisms to progressively isolate the style- relevant subspace from the entire representation space, improving the expressiveness of the style
HHee we select Fnglish, the most widely used language globally, and Chinese, the language with the largest number of native speakers, as our two examples. For dataset details, please refer Section 5.1
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and ensuring that the semantics of LLMs remain unaffeeted. Finally, we introduced benchmark to evaluate the response quality of stylized QA. DRESS shows significant improvements over SOTA baselines, including SFT. prompting, and other representation editing methods, demonstrating the effectiveness of our method.
RELATED WORKS
Recently, there has been line of research embarking on controlling the behavior of LLMs through
representation editing, most of which focuses on truthfulness enhancement (Zou et al. 2023
Li et al.
20236), knowledge editing (Todd et al. [0233] Hernandez al. (202)) etc. This technique is based on the linear representation hypothesis (Elhage et al. 2022) supposing that most high-level concepts
represented linearly as directions in LLMs, which is theoretically supported by the approximate
orthogonality assumption under overparameterized networks (Wang & Zhu (023)). and practically
demonstrated by the success of linear probing techniques (Alain & Yoshua 2016 Belinkov 2022)
The primary objective of representation editing is to identify some steering vectors and add them to some layers of the forward pass of LLMs introduce certain attributes (i.e., language style this work or truthfulness, etc.) into the LLM outputs. Mean-Centring (Jorgensen et al. (202)) computes the steering directions using the mean difference between paired activations. RepE (Zu et al.) 2023
Representation Engineering) applies PCA to the set of difference vectors and selects the principal component as the steering vector. CCS (Burns et al., 2023 Contrast Consistence Search) obtains the steering vector through the probing vector that well classifies the activation pairs. ITI (Li et al., 2023b Inference- Time Intervention) further enhances CCS by locating attribute-relevan attention heads. However, due to the intricacy of language attributes, it is insufficient to depiet them with a single direction as in the aforementioned works. TrFr (Chen et al. 2024b Truth Forest) proposes a specific combination of several vectors under orthogonality regularization enhance the expressiveness of the target attribute. Nevertheless, none of the methods above attempt to explicitly disentangle the attribute subspace from the entire representation space to avoid affecting the original semantics. Moreover, previous works overlook the varying importance of different attribute components across various contexts, which can adversely affect the quality of the outputs. In this work, we propose DRESS to solve the problems. DRESS comprises three progressive mechanisms to isolate the attribute-relevant subspace and conduct adaptive editing in order to enhance the expressiveness and flexibility of steering, meanwhile ensuring the semantics are preserved.
3 PRELIMINARIES
Problem Formulation In this paper, we aim at making LLMs respond to user queries in a specific style. Rigorously, given each user query q. an LLM (-) to respond the query with ((g) as the original response, and target language style depicted by QA examples {ę: ) where a, are all stylized responses (i.e., O; S), our objective is to edit the representation space of LLM and obtain a new response M') of user query q, where the response M'(q) is of the same style with S (i.c., M')g) S).
Representation Editing Here we rigorously introduce where representation editing takes place in the transformer-based LLMs. To set notation and contexts, we first briefly introduce the transformer (Vaswani 2017) architecture adopted by mainstream LLMs. A transformer based LLM comprises several stacked transformer blocks, each composed of a multi-head self-attention (MHA) block and successive MIP layer. Specifically, a transformer block could be expressed as follows:
(++1) MLP(MMA(( MLP +
(x(Attn, ()))
(1)
h=1
has been demonstrated that the MHA block and the feed-forward network (FFN) perform different functions in LLM,, where MHA blocks tend to encode language attributes (Clark 2019) while FFNs tend to conduct reasoning (Geva et al., 2020). Hence, it is more reasonable to edit representations in MHA blocks to minimize the influence semantics. Specifically, the edited steering vector is attached after the Attn operator and before W following Li ct al. (2023);; Chen et al. (024b))
H
# (1+1) 1) MMP((MHA(Z = MLP
+
+x (h.1))),
(2)
h=1
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4i
Question
Style evsntt Attention hcad
Styleimebeant
Abtontion
Responses
a
AU
d'tz
Ordinary Responses
Lthhead- Allention
1o]
01V1A
Buy
Target Style QA
scripds, plays,..
heads
Probing
Probing
Σ
V
0g Pk
qi
Question
u2
V1
Stylued
Responses
0
X = x
a;Vi
p(u = Sigmoia(
22
(1) Dataset Construction
(2) Attention Head Filtering
(3) Style Subspace Filtering
(4) Adaptive Editing
The overall pipeline of DRESS. We first process the target-style QA dataset into form suitable for solving the steering vector. Next, we use probes to filter out the attention heads most relevant to the style and further disentangle the style-related subspaces within the representation space of these heads, where the steering vectors are computed. Finally, during editing, we apply an adaptive editing strength mechanism control the magnitude of different sub-directions the style subspace, optimizing the editing quality while avoiding negative impacts on the output semantics.
Figure 2:
where (h.1) E Rd is the steering vector to be solved for editing the h-th head in /-th layer, and we denote u (h.5) E IRd as the original activation of /itth head in /-th layer, i.e., (h.1)) = Attn (x(").
METHODS
In this section, we introduce how DRESS solves the steering vectors and conducts representation editing for stylized outputs without compromising the semantics. Specifically, the pipeline is shown Fig 2
and we introduce the details as follows.
4.1 DATASET CONSTRUCTION
To conduct effective representation editing, it is necessary to investigate the differences between the activations of QA samples with different styles but the same semantics for deriving a style- relevant steering vector. The target style is inherently implied by QA examples {gi, a.) which are collected from literature, scripts, or chat records. Therefore, to compute the steering vector, we also need to obtain the ordinary style of these responses (i.e., the style LLM generates), thereby constructing the dataset = { 44. a. a,"" ] to solve the steering vector, where a, is the response to qi in the ordinary style and a, is the collected target style response. To obtain the ordinary style expression of a (i.e., a; without altering its semantics, we apply GPT-4 to rewrite at to align with the typical LLM language style (i.e., modern daily language style). The specific prompt used for this task can be found Appendix C.I
Additionally, since the dataset often originates from scripts and literary works, the language style of the queries tends to be biased. To mitigate the influence, we introduce another general-purpost LLM QA dataset (e.g.. Alpaca TTaori et al., 2023), MOSS (Sun et al. 2024g) D' {q's a,' " to diversify the style distribution of the queries. Specifically, the general pprrppse QA dataset already contains the ordinary style QA data pair (i.e., L., ",,a;,), so we need to construct corresponding target style responses a;' to perform data augmentation. Here, we again prompt GPT-4 to generate the target style responses, with brief introduction of the target style and randomly sampled target style responses a, from the collected dataset 'D as few-shot examples. The detailed prompt can be found in Appendix C.2 Finally, the dataset are constructed
D' sized N n n'.
4.2 ATTENTION HEAD FILTERING
Recent works (Ge et al. 2024 have demonstrated that different attention heads perform different functions in LLMs. Therefore, identifying the attention heads most closely related to styles is crucial for conducting semantic-isolated representation editing. Probing, highlighted in works like (Alain Yoshua 2016 Conneau et al. 2018 Belinkov 2022). has emerged as a robust and effective technique for analyzing the internal functions and behavior patterns within LLM representations. Our key idea is to train linear probing classifier on the activations of LLMs to discriminate between the ordinary and target language styles. Since each pair of responses in our dataset (i.e., a a.
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where a: (h.l) is the editing strength of the corresponding basis o (hi)) in the style subspace, and (h.l) Published as a conference paper at ICLR 2025
shares the same semantics but only differs in style, determine whether attention head is style-relevant based on the probing accuracy of the style classification task.
Hence, in DRESS, we define the probe ((u( Sigmoid u (().)))) for each head h in each layer 1 of the LLM to detect the style-relevance of the activations. For each sample, concatenate the queries qi and responses a; and extract the activations at the last token, where the semantics are completely encoded and ensured to be the same for each pair of aj and a,", Then we create
a probing dataset (()) = {(u (h)) }4)} for each head in each layer, where indicates whether the current activation originates from the ordinary or target style. Specifically,
D/e² (M ) y* ))", (
"i=1"" 1.y 0.
Next, randomly split each dataset into training and validation sets in 4:1 ratio, fitting the binary linear classifier p(-) on the training set. We select the attention heads with the top-H validation accuracy as style-relevant and conduct editing within those heads.
4.3 STYLE SUBSPACE FILTERING
Given the selected attention heads, we aim to further filter out the style-irrelevant components and disentangle the subspaces that are more closely related to style for editing. Since the activations in the nigh-dimensional space of LLMs can be assumed to be approximately orthogonal with high probability (Wang & Zhu 2023 Ortiz-Jimenez et al. 2023). we can hypothesize that the language styles reside in subspace orthogonal with semanties. Given that our positive and negative sample pairs (i.e., (qi ag (q:; a," )) differ only in style while maintaining consistent semantics, their activation differences (i.e., ou (h.f)
(h.h..) u (h.1)+ u (h.1)) primarily capture the variation in style, with minimal inclusion of semantic or other noisy components. Thus, DRESS proposes to isolate the style-relevant subspace by denoising the space spanned by these activation differences.
Specifically, we first collect the activation differences of all sample pairs, denoted AU
[ōu (h.1) 5u2 (h.1) .... 5uw (h.0)T E Nxd Then we apply Singular Value Decomposition (SVD) on (U(,), and select the top-K singular vectors with the largest singular values to form the orthogonal
basis of the style subspace. thereby capturing the most representative style-related features while filtering out irrelevant noises. Rigorously,
(h shh.))hhhhh))0000000 Σ
(h,))
0:8 (h.)) (h.l)
0,8
(4)
where (h.1) Rd is the singular vector and o; IR is the corresponding singular value satisfying Vi j, > og. Finally, the editing conducted in the style subspace spanned by u (h1) as follows:
K
(1+1) = MLP + h-1
(x(ttaa(((()
(1)
(h.1) (1)))))
(5)
especially, for attention heads that have been filtered out in the previous step, a; = 0.
4.4 ADAPTIVE EDITING
Since different style components (e.g., tone, formality) may have varying importance or influence depending on the specific context, uniform adjustment would fail to capture these subtleties. Thus, in this subsection, we introduce our adaptive editing strategy, designed with the adaptive strength
coefficient a; (h.l) in 5. This coefficient comprises two key components: global editing strength and an adaptive scaling factor. The global editing strength reflects the population-leve steering intensity across the dataset, capturing the overall style shift observed in the majority of the samples. Specifically, the global editing strength, denoted as g(hh1), is measured by the projection of the
mean difference between positive and negative activations (i.e., 5u (h.J) ΣN (h.) onto the
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orthogonal basis v. Ad of the style subspace:
8 ((hh1) = (ōr (h.1) Bu. 87
|| cos( cos(ōu (ou (h.0)
ou
(6)
The adaptive scaling factor is dynamically determined during the generation of each token on each subspace basis. For each token's current activation (h.),, observe the difference between (h.1) and the mean activations of all target style samples (i.e., ((h.1)++ = N Σ u (1.))t) under the style subspace projection. This projection represents the approximate difference between the current token and the target style, which dictates how much strength we should further attach to each basis and guide the token's activation closer to the target style in pappooorppp manner, leading to more accurate and flexible stylization. Specifically, the adaptive scaling factor is designed as follows:
T/ (h.l) cos((a (h.)) u (h.))) A.1).
(h.1) computes the correlation between the style differences and the corresponding basis,
where 7 depicting how much strength should be augmented to derived from the current edition. 7: is
further attached to the global strength to conduct adaptive steering with (1 + 7/3 )
(1.10) ((h,)) Finally,
we introduce hyperparameter À to control the overall style edition strength:
(h.I) X(1 + 7 (1
a;
(8)
This strategy enables the model to control its editing strength in real-time generation, aligning more closely with the desired style while preserving the integrity of the original content. We also present the algorithmic pseudo-code of DRESS in Appendix
5 EXPERIMENTS
5.1 EVALUATION BENCHMARK
Datasets We constructed the evaluation benchmark with representative language styles in Chinese and English, i.e., Shakespeare-style and Dream of the Red Chamber-style. These styles exhibit significant differences from contemporary language in tone, idiomatic expressions, historical context, etc., making them easy to observe and evaluate. The Shakespeare-style benchmark aims to mimic the language style in Shakespeare's 's works, with the dataset derived from the original texts of his play following (Xu et al. 2012). The QA pairs are constructed from excerpts of single-round conversations between different characters in the plays. Dream of the Red Chamber a lengthy fictional novel published in the 18th century and is one of China's 's Four Great Classical Novels. The Dream of the Red Chamber-style benchmark aims to replicate the dialogue style of its characters, with the dataset sourced from the original novel and adapted scripts from film and television. Similarly, the QA pairs are constructed from individual character dialogues in these works.
Additionally, mentioned in Section 4.1 for each dataset, we incorporated the general question- answer dataset (i.e., MOSS (Sun et al. 2024) in the corresponding language) to address the bias in question style distribution. We then randomly divided each of them into training and testing sets at a ratio of 10:1. The training set is used to solve the stylized QA model, while the testing set only utilizes the questions as the test queries evaluate the model performances. The detailed statistics and the examples of the datasets are introduced in Appendix
Evaluation Metrics A successful stylized response not only needs demonstrate the target style, but also ensures that the original semantics are preserved and the language remains fluent given the inherent uncontrollability of LLMs. Hence, following JJin et al. (2022), we evaluate the quality of the stylized responses in three aspects, including style intensity, semantic preservation, and fluency:
Style Intensity (SI): we leverage a separately trained style classifier to distinguish whether the response could demonstrate the target style (Shen et al. 2017). Specifically, the classifier is fine- tuned on BERT (Dellin et al. 2018) models Jusing the responses of the target style as positive samples and those of the ordinary style negative samples. The style intensity is calculated as: it. Responses classified as the target style ranging from [0, 1].
All responses
https: : /www. shakespea reswords. newwords.ccm/
We use BERT-base for English dataset and Chinese- BERT-wwm-ext Cui al. 2021 for Chinese dataset.

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Semantic Preservation (SP): Semantic preservation aims to reveal whether the stylized responses semantically deviate from the original output. Hence, we apply the averaged cosine similarities between the semantic embeddings of the original and the stylized responses of LLMs (Fu et al.) 2018), encoded by BGE (Chen et al. 2024a) embedding model. This score ranges from [0, 1j-
Fluency Score (FS): We also utilize the perplexity metric calculated by the original LLM (i.e., before representation editing) to depict the language fluency. Since perplexity ranges from [1, o0), differs in an exponential magnitude, and is negatively correlated with fluency, we design the fluency score of response as 14log PPT. This score ranges from (0, 1] where the values in more uniform manner, and the higher the score, the more fluent the response. To depict the population-level performance, we report the mean fluency score across all stylized responses.
re-scaled
We also design an objective overall assessment score (OA) using the products of the three metrics (i.e., OA SI*S the higher the better), balancing the trade-off effects between them. Furthermore, we utilize GPT-4 (Achiam et al. 2023) to rate the stylized responses comprehensively, with scores ranging from 0 to I0 The averaged GPT-4 rating is reported for subjective overall assessment.
Baselines
We adopt the following state-of-the-art approaches as our compared baselines.
Few-shot Prompting leverages the in-context learning ability to achieve stylized responses. Specif- ically, we use a well-crafted prompt to describe the target style (See Appendix C.3 for detailed prompts), alongside randomly sampled 3-shot examples from the training set as the demonstrations.
Supervised Fine-Tuning (SFT) incorporates the stylized QA samples in the training set as supervision and tunes the model parameters adapt the outputs to the target style. Here we apply the state-of-the-art PEFT algorithm LoRA (Hu et al. 2021 as our fine-tuning strategy.
Representation Editing aims to solve generalizable steering vectors attached to LLM internal activations for style editing. Here we include several saate-of-the-ee representation editing methods as baselines, which have demonstrated superior performances on controlling truthfulness, such as Mean-Centring Centring (Jorgensen et al. 2023). RepE (Zou et al. (023). ITI (Li et al. (023b)), and TrFr
(Chen et al. 2024b). The details are discussed in Section
2023) as our base LLM to experiment on. The experiments conducted on machine equipped with 8 NVIDIA-RTX3000- 24GB GPUs All the hyperparameters (e.g., the number of selected attention heads H, editing strength X, etc.) are tuned via grid search. See Appendix D for more details.
Implementation Details We apply Qwen- 1.5-14B-Chhs (Bai et al.
5.2 EXPERIMENTAL RESULTS
Quantitative Analysis Table Dableepreseer presents the performance of various methods on two stylistic QA evaluation benchmarks. In addition to conventional baseline methods, we also include a comparison with DRESS" as an ablation study, which removes the adaptive scaling factor ? during inference,
(h.l) = x.g(h.)) It can be observed that our method demonstrates significant performance improvements over all previous approaches, including few-shot prompting, supervised fine-tuning, and all conventional representation editing methods. On Shakespeare-sty benchmark, DRESS exhibits 7.84% improvements on overall assessment and 1.37% on GPT-4 rating compared to the best-perfir pest-perfornin baseline. On Dream of the Red Chumber-style benchmark, the improvements reach as high as 23.8% on overall assessment and 4. 4.19% on GPT-4 rating, respectively, demonstrating the effectiveness of our method. Below are some key findings:
with a fixed editing strength a.h.))
Conventional representation editing methods are not sufficient for stylized QA. Though demonstrated effective in enhancing LLM truthfulness, most conventional methods cannot reach the performance of few-shot prompting. The performance gap can be attributed to the ignorance of disentangling style from semantic, which can potentially damage the original semantics and even affect the general language ability of L.Mss. ITII attempts to locate style-relevant attention heads to edit within, but still suffers from noises underlying the edited representation space. Hence, we think it is crucial to isolate the style subspace for representation editing stylized QA tasks.
Pleasse refer Appendix C.4 for the rating prompts.

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Table I: Experimental results on two stylized-QA benchmark, Dream of the Red Chamber-style and Shakespeare-style. The stylized responses are evaluated through style intensity (SI), semantic preservation (SP), fluency score (FS), and overall assessments, including objective assessment (OA SI*SP*FS) and GPT-4 rating. For all metrics, higher scores indicate better performance. The first and second dest-performing methods are respectively highlighted in bold and underline.
Dream of the Red Chamber-style (Chinese) OA (%)
Shakespeare-styl (English) SP (%)
Method
SI (%6
SP (%)
ES (%)
GPT-4
SI (%)
FS (%)
OA (%)
GPT-4
Prompt
93.0
66.2
36.8
22.7
7.48
98.0
69.9
37.8
25,9
8.58
SFT
85,3
69.0
40.0
23,5
7.19
95.5
69.8
36.8
24.5
8.08
Mcan-Centrinn
77.5
63.6
31.4
15.5
5.63
94.5
71.5
35.3
23,9
8.26
RepE
58.5
67.7
42.2
16.7
5.64
94.5
65.2
34.3
21.1
6.91
Trlir
99.0
69.7
33,9
23.4
6.96
99.3
70.8
38.2
26.8
8.73
ITI
84.7
70.3
36.7
21.8
7.64
99.5
70.5
36.3
25.5
8,60
DRESS DRESS
89.0
70.9
37.8
23.8
7.71
99.0
71.2
38.2
26.9
8.63
97.0
70.8
42.4
29.1
7.96
99.5
73.3
39.6
28.9
8.85
Table 2: Case study for qualitative analysis. Two cases for Shakexpeare (Q1: general QA, Q2: plot-related QA) and one case for Dream of the Red Chamber (Q3: plot-related QA) are presented.
DRESS Respouse
ITI Response
Unedited LLM Response
[Sholespeure-stlluy QT: Which of these tourist attractions is the most visited?
Verily, the statement thou presenteth is incomplete. require more data, there are many tourist attractions world wide, each with their own allure. Kindly, furnish with land region, that may direct search therounto.
Verily, I shall not dilate upon that which is super- Blucus. The question acketh which tourist's 's joyance is ofthest visited, and thus, without further ado, I imparichh unto thee, that which the folk mass doth frequent most:
would need more information about specific
attractions you referring to in order to determine which the most visited. Please provide with a or some details attractions in question.
of China, the Colosscum of Rome, or the Parke Royal olde Landinium. Verily, these be the tripartie, whereof People's will doth hend course.
[Shakeqpeor--ttlee Q2: Pardon this fond:, and by my wode I sweare I newer more will breake an nath with thev.?
Verily, do accept thy penunce and thy that thou ne's er again breake bond with me. Thy word is as a gem, precious and true, and I do hold it in the hallowed hallows of mine own heari. So, lot proceed in this our discourse, without further let hindrance.
Verily, thou hast repented of thine ill, and forsnoth, shall no more entreat an oath from thes. Felicite of thy contrtion doth solace me. In peace we rest.
Given your rath, I forgive your mistake and hope you will never break my oath again.
Wream of the Red Chamber-style] (3: 1E HEBUIE SOOL T HE BE 1
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Adaptive editing enhances the quality of style editing. Comparing DRESS* with DRESS, we observe significant improvements across all metrics, highlighting the effectiveness of adaptive editing. On the one hand, using adaptive editing strengths for each style basis substantially improves the expressiveness and flexibility in capturing style, thereby optimizing editing quality. On the other hand, context-award aware strength adjustment ensures the appropriate intensity for each token, preventing over-editing or under-editing, thereby improving robustness.
Dream of the Red Chamber-style is a harder benchmark. The results show that it is not easy to reach a high SI score and GPT-44 rating on this benchmark, and the performance gap is obviously more significant. The difficulty lies in its complex mix of classical Chinese, fewer similar corpus seen during LLM pretraining, and very deep cultural references, which require nuanced understanding beyond language rules. This makes Dream of the Red Chamber more challenging to emulate. Hence, in further analyses, we mostly use this challenging task for observation.
Qualitative Analysis We present several QA cases for qualitative analysis in Table We can observe that DRESS can provide responses of significant stylistic language features and meanwhile shows higher consistency with the original response compared with ITI. For instance, both DRESS and unedited LLM avoid listing examples in the response to QL, whereas ITI attempts to suggest attractions like the Great Wall. In Q2, DRESS provides more metaphors (e.g., as a gem, precious and true) and rhythmed arrangements, which is more likely to be in Shakespearean play. Meanwhile, ITI lost the semantics of never break my oath in the original response, while DRESS depicts it with without further let or hindrance. For more cases, please refer to Appendix
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DRESS
Varying Style Editing Streagth.
0.5
1.0
Proemation
88
9.40
0.5
Overall
0.2
0.7
Blurnyy
9.35
SWd
8.6
9.30
0.1
0.5
0.25
2
2
Figure 3: Sensitivity analysis of varying style editing strength of DRESS and ITI on Dream of the Red Chamber-style benchmark.
ITI
DRESS
Varying R. Selected Attention Heads H
Semerik Procrcation
0.25
Style Intensity
0.9
Oveall
Titenty
0.35
0.20
0.5
0.30
0.5
32
64
M5
128
32
64
96
128
32
64
96
128
64
95
128
8. Attention Tledd
Artention Head H
Amention Bead H
A.teettion Hend H
Figure 4: Sensitivity analysis varying the number of selected attention heads H of DRESS and ITI on Dream of the Red Chamber-style benchmark.
5.3 ANALYSES
Effects of Editing Strength In this subsection, we analyze the impact of different editing strengths (i.e., A) the performance of various methods, as illustrated in Fig We compare DRESS with the most representative conventional method, ITI. The results show that DRESS consistently outper- forms ITI across all À values on the overall metric. Both methods display a pattern where overall performance initially improves and then declines as À increases. This behavior is due to the inherent trade-off between style strength and the other two metrics (i.e., semantic preservation and fluency). However, as editing strength increases, DRESS maintains consistently higher fluency and preserves semantics more effectively compared to ITI. This is because ITT does not further disentangle the style subspace of selected attention heads, which results some semantic damage during the editing process. Furthermore, even lower editing strengths, DRESS exhibits stronger style intensity than ITI. This can be attributed to our adaptive editing strategy, dynamically adjusting the strength according current contexts and providing some remedy when the strength is insufficient. These results demonstrate that DRESS not only achieves better performance across all metrics but also exhibits greater robustness across various levels of editing strength.
Effects of the Number of Selected Heads We further analyze the impact of varying number of selected heads (i.e., H)) as illustrated in Fig It can be observed that DRESS maintains stable performance the number of attention heads (i.e., H) increases and consistently outperforms ITI. In contrast, ITI shows sigmificant decline in semantic preservation and fluency with larger H. This is because, more style-irreeevant contents are incorporated H increases, leading semantic distortion and degraded language quality during editing. In comparison, DRESS applies additional subspace filtering denoise the representation space the selected heads, preserving semantic integrity and enhancing overall performance.
Are Style Subspaces Really Relevant to Styles? To better understand whether the learned style subspaces are indeed style- relevant, we randomly select an edited at- tention head and project the representations of ordinary style (i.e., u u)) and target style (i.e., u+) samples onto the top-2 singular di- rections of the style subspace (V1, Vz). We then compare these projections with those projected onto the top-2 singular directions of the unselected style-irrelevant subspace
Tagal Polut Dedian Bacel
18
JA 52 30
0.75
0.75 -365
4:21 008 033 0.50
(6) Style-eelevar Sabequac
(b) Style-iroloon Subspace
Figure 5: Projections of activations from target style ut and ordinary style w to different subspaces.

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(UK+1, UK-2), and plot their respective kernel density estimate distributions, shown in Fig and (b), respectively. It can be observed that the samples of different styles exhibit distinct distribution differences in the style subspace, while their distributions in the style-inreeevant subspace nearly identical. This indicates that the selected subspace indeed highly related to styles, demonstrating that DRESS successfully isolates style from semantics, enabling more precise style control.
Probing Accuracy across Layers To investigate whether different attention heads in various layers of the LLM have distinct sensitivities to language style, we examine the probing accuracy of each layer on the validation set, shown in Fig. 6 From sub-figure A, we can observe that no specific layer generally focuses more on language style. Instead, all layers exhibit some sensitivity to language style. This indicates that
2.500
language style is modeled in both shallow layers of LLMs for learning inter-word correlation and deeper layers for the reasoning and decoding processes. In sub-figure B, we observe that not all heads in each layer are attentive to language style. Only subset per- forms style learner function. In summary, we found that the LLM's attention heads are ubiquitously sen- sitive to language style across all layers, with certain heads in each layer specifically focusing on it. This finding supports the assumption behind our design of style-relevant attention head filtering.
Figure 6: Probing accuracy on validation set across various layers. (A): the mean and std of the probing accuracy of all heads in each layer. (B): heatmap of the probing accuracy for all heads across different layers, sorted row-wise by accuracy.
For more analyses on generalization to other models and applicability to low- resource scenarios, please refer to Appendix B
More Analyses
CLOSING REMARKS
In this work, we introduced DRESS, a novel train-free framework for efficient stylized QA via style subspace editing in LLMs. Our approach disentangles the style-relevant subspaces within the representation space of LLMs, enabling adaptive and controllable stylization via representation editing while preserving semantic integrity. We construct two distinct benchmark datasets, Shakespeare-sty (English) and Dream of the Red Chamber-style (Chinese), for comprehensively evaluating the quality of stylized responses. Through adequate experiments on the two datasets, we demonstrate that DRESS significantly outperforms existing methods, including prompting, SFT, and conventional representation editing techniques. Our results confirm the effectiveness of DRESS in enhancing LLMs with flexible style control, making it particularly valuable for developing conversational agents.
Despite its strengths, DRESS has some limitations that warrant future exploration. Although DRESS establishes solid foundation for language style adaptation, building scenario-specifee conversational agents (e-g., a chatbot embodying a historical figure, an assistant for medical prediction and counseling (Ma et al. 2023)) still requires careful modeling of character personalities and the implementation of dialogue memory capabilities. This is an important step towards developing more systematic and humanoid agents, and vetrieval-auugeetee generation (RAG) techniques have been widely researched to achieve this goal (Zhang et al.) 2024 Xu et al. 2024). We regard them as our significant future work. Moreover, due to the limitation of our computation resources, the scalability to larger LLMs (e.g., 100B+) has not been validated yet. We also look forward to exploring the effectiveness of DRESS those models from self-play perspective, and we hope to validate this in future work.
ACKNOWLEDGMENT
This work is supported by the National Natural Science Foundation of China (No.U23A20468).
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A ALGORITHM FRAMEWORK OF DRESS
Alg. I shows the detailed procedure of how DRESS solves steering vectors and conducts adaptive representation editing.
B FURTHER ANALYSES
Generalization to Other Models To validate the generalizability of DRESS to other base models in different size, here we conduct experiments on LLaMA-3-SB (Dubey et al. (024) with our proposed benchmarks, The results are shown in Table 3 Results demonstrate that method still consistently performs well on LLaMA-3-8B Especially for Dream of the Red Chamber benchmark, is observed that prompting method cannot conduct successful stylization probably due to the lack of pretraining corpora of this style and even fails to imitate few-shot samples. While our method significantly outperforms the SOTA baselines and achieve consistently better stylization quality.
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Algorithm The representation editing procedure of DRESS.
1: Input: style sample sataset D {q., a:). LLM M(-), user query q.
2: Output: stylistically edited LLM M'),), stylized response a = M)
3: D {qi;0, a; ;a,
Construct corresponding ordinary style response
4: D' < {q. a, ",,",, D
Augment the dataset with general purpose QA
5: (h,l)- - -Miqi ai )(hl) ui (1+0) - M(q. a.
(h.1) (b.1) - M(q) (h.1) y 1. y = 0
6: t - -[(u .2* ) o (1.1) (0(00)000000 .
:-1 u
ii
Dataset for probing style-relevance
7: A {(h, toppt/o-H(HA)))) MiigoSSi u, (1.11))) yi))}
Filter style-relevant attention heads
8: 8u, (hil) u; (h.))+ u (h-1) AU [ōu (h.1)
,... ,unx
9: V(h,0) top-K, SVDD(U (h, A Topp- singular vector the style subspace
10: ((h - -(Tu (1.1) _., (1.) cos( (u (1( - -m(h)),, h.l
11: O -X(1 + ) (h,1) A: a (hhi)) - O,, $ A
(h.f)
12: M') + -z(++)) = W "A(Attn((x!) + Σ a
(b.0)
0! ((hi)i)) Adaptive editing
13: a (4)
return M(.,, a
Table 3: Experimental results on LLaMA-3-8B.
Dream of the Red Chamber-style (Chinese)
Shakespeare-style (English)
Method
S1 (%)
SP (%)
(%)
OA (%)
GPT-4
SI (%)
SP (%)
FS (%)
OA (%)
GPT-4
Prompt
38.8
71.4
38.1
10.6
5.44
99.8
69.1
40.6
28.0
8.85
ITI
82.0
68.1
37.9
21.2
7.25
99.5
73.2
43.1
31.4
9.08
DRESS
85.3
71.1
41.4
25.1
7.53
100
75.0
43.7
32.7
9.14
Low-Resource Style Adaptation To validate whether DRESS still applicable under the "data hunger" scenario, we tested the performance of DRESS using 50- 10-10-1% samples of the training set respectively. The results are shown in Table 4 Results demonstrate that though the performance gradually decreases as the incorporated data size shrinks, we also find that DRESS can still perform better than prompting methods even using only 1% data (i.e., around 40 samples). In contrast, prompting methods fail to capture the style patterns from the-middle problem, thus leading to performance decay when increasing in-context samples from to 40. This demonstrates that method is applicable for low-resource style adaptation and can perform even better when the samples are more sufficient.
samples and suffer from the lost-in-
C SYSTEM PROMPTS
This section presents the system prompts used in dataset preparation, baseline prompting methods for stylized QA benchmark, and GPT-4 evaluation. Prompts crafted in the corresponding language for both datasets. The few-shot examples in all prompts are randomly sampled from the training set.
C.I SYSTEM PROMPT FOR CONSTRUCTING ORDINARY STYLE RESPONSES FROM TARGET STYLE QA DATASET
Shakespeare-style Benchmark
I will give you a sentence from the original text of Shakespeare's 's play. Please translate this sentence into modern English style and tone while maintaining its semantic consistency, erasing Shakespeare's own language characteristics. Please note that do not translate each word individually, but rather transform the sentence modern English. Please only output the style converted content of this sentence and do not output any extra characters. This sentence follows: [INPUT SENTENCE]
whole into the ordinary style of
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Table 4: Performance of DRESS using 50-10-1% of training set data.
Dream of the Red Chamber style (Chinese)
Method
SI (%)
SP (%)
FS (%)
OA (%)
GPT-4
DRESS-100%
97.0
70.8
42.4
29.1
7.96
DRESS-50%
97.0
71.1
41.3
28.5
8.02
DRESS-10%
98.3
70.0
40.5
27.9
7.88
DRESS-1% %
96.5
69.0
37.0
24.7
7.82
Prompt-3 shot
93.0
66.2
36.8
22.7
7.48
Prompt-19
83.8
70.0
38.5
22.6
7.27
Dream of the Red Chamber-style Benchmark
SA  HEEHH THE AAEERA X BE [INPUT SENTENCE]
C.2 SYSTEM PROMPT FOR CONSTRUCTING TARGET STYLE RESPONSES ot FROM GENERAL QA DATASET
Shakespeare-style Benchmark
I'll give you ordinary modern English sentence. Please style it while keeping its semantics unchanged and translate it into the style and tone of Shakespeare's original play, while maintaining semantic consistency. Please note transform the sentence as a whole into the style of Shakespeare's 's play. Please only output the sentence style converted content and do not output any additional characters. Here are some examples of sentences from Shakespeare's ' original plays. please refer to Shakespeare himself and the language characteristics of that era.
translate each word individually, but to
[Example 1] But looke, the Morne in Russet mantle clad, Walkes o' re the dew of you high Easterne Hill, Breake we our Watch vp, and by my aduice Let vs impart what we haue seene night Vnto yong Hamlet.
[Example 2] That were the Slaues of drinke, and thralles of sleepe?
[Example 3) Here from Verona art thou banished: Be patient, for the world is broad and wide. [Example 4] You giue your wife too vnkinde a cause of greefe, And 'twere to me should be mad at it. This sentence is follows: [INPUT SENTENCE)
Dream of the Red Chamber-style Benchmark
Stw. HD SEANAAAATTTTTT NNNATAAAAAATTAETE H
T 18
* I
1E -
33
A. B E
TF
4
MEEEAAAAA [INPUT SENTENCE]
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C.3 SYSTEM PROMPT FOR THE BASELINE PROMPTING METHOD
Shakespeare-style Benchmark
For the following question, please answer it using the language style of Shakespeare's 's plays. Here are some examples of sentences from Shakespeare's 's original plays, please refer to Shakespeare himself and the language characteristics of that era. [Example 1] That were the Slaues of drinke, and thralles of sleepe?
[Example 2] Here from Verona thou banished: Be patient, for the world is broad and wide. [Example 3] You giue your wife too vnkinde a cause of greefe, And 'twere to me I should be mad at it.
This question is as follows: [INPUT QUESTION]
Dream of the Red Chamber-style Benchmark
<
THINAAITSSHJJSSSEAAAAAAAAMTEEEEE 7 #
2 -
3 -
AE
H (I # (INPUT
QUESTION]
C.4 SYSTEM PROMPT FOR GPT-4 EVALUATION
Shakespeare-style Benchmark
Now I will give you a question and answer; you need to rate the answer sentence. The specific requirements are:
1. The language style of Answer needs to be consistent with the language style of characters in Shakespeare's plays, rather than the style of modern English. We do not require Answer to be consistent with the language style of a specific character in Shakespeare's 's works, but it needs to conform to the overall language style of all characters in Shakespeare's plays.
2. The semantics of the Answer need to match the Question and be able to respond smoothly and completely the sentence Question. Note that the Answer does not need reflect seman- ties related to the plot of Shakespeare's 's play, even if it involves content completely unrelated Shakespeare's 's works, as long as can answer the Question, can meet requirement 2. 3. The scoring range is integers between O and 10. If you think Answer has completed requirements and well, you should give higher score; otherwise, give lower score.. Your response should only include a rating for Answer (an integer) and should not contain any extra characters. The question and answer pairs are as follows: Question: [Question] Answer: [Answer]
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Dream of the Red Chamber-style Benchmark
A AnnSa A
#
1. Answerfl 12 i -
(
X K * R Answerr
Answerf]] X X
J Qu
N F.. Ansswrr 1 x 14 14 1
# R
A | T Question. AD 7
3. t 1
A # #t.
Answer//ii1
(~ T
| x Xm F:
Question: [Question]
Answer:
[Answer]
D IMPLEMENTATION DETAILS
For SFT, the rank of LoRA is set to 8, and the training epochs is set to 3. We apply cosine learning rate scheduler with a warm-up stage of 10% of total steps, and the maximum learning rate is set 5e-5. The batch size is set to 32, and only Wg We. W W, fine-tuned. For DRESS the number of selected attention heads H = 64, the number of the style subspace basis K = 16, and overall editing strength À 3. For ITI. the number of selected attention heads editing strength a = 3. For TrFr, the number of selected attention heads H 48, the orthogonal regularization coefficient editing strength
64, and the
5e 2, and the editing strength a 40. For Menn-Cen,ring, the 3, and the edited layer = {17, 18, ... 22). For RepE, the editing strength = 4, and the edited layer {15, 16.... ... 22].
To better observe the performance of semantic preserving, we set the decoding temperature to 0 to achieve deterministic outputs, thereby eliminating the instability in metric computation caused by random sampling.
E BENCHMARK DATASET DETAILS
We present the detailed size and composition of two evaluation benchmark datasets in Table
Table 5: Dataset Statistics.
Dream of the Red Chamber
Shakespeare
Sources
Train
Test
Train
Test
Target Style QA
2589
200
2038
200
General QA
1500
200
1960
200
Total
4089
400
4098
400
We also present some data examples from the training sets in Table and 7
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Table 6: Shakespeare-style Dataset Examples. in Sec
[*] denotes that this response is generated as introduced
Sources
Question
Target style response (+)
Ordinary Style Response (-)
um scarce in breath my Lord.
No Maruell, you haue bestir'd d your valour, you cowandly Rascall, nature dis- claimes in thee: Taylor made thee.
[*] No wonder you' 'e been acting
Target
brave, you cowardly scoundrel; even ture disowns you-a tailor must have created you.
Dispute like a man.
shall do so: But must also feele it as man; cannot but remember such things were That were most precious me: Did heauen looke on, And would not take their part?
[+] I'll do it, but still need to feel it like man. How can not remember the things that were most important me? Did Heaven it happen and do nothing? [*] The verse doth not explicitly denote the kind of magick which didst empower the citics. However, it doth imply that the cities were quickened by some craft, which may refer to sundry forms of cn- ergy conjure, be it from the bowels of the earth. the heart of mighty atoms, or the gentle breath of sun and wind.
What kind of technol-
The verse does not specifically mention what kind of technology was used power the cities. However, it implies that the cities were powered by technol- ogy. which could refer to various forms of energy production such as fossil fuels, nuclear power, or renewable sources like solar or wind energy.
General
ogy was used power the citics mentioned in
verse one?
have budget of $2000 for this trip. What are my options?
[*] I thank thee for divulging thy fiscal means to me. With purse of two thou- sand crowns, thou hast many choice at thy disposal! Pray, canst thou tell mc somewhat of thy favored sojourns? For- south, dost thou prefer a sojourn by the shore, sojourn in the bustling city, exploration of nature's 's realms?
Thank you for sharing your budget with me. With budget of $2000, you have lot of options available to you! Can you please tell me bit about your preferred travel destinations? For example, do you prefer beach vacation, a city break, or exploring nature?
Table 7: Dream of the Red Chamber-style Dataset Examples. generated as introduced in S.c
[*] denotes that this response is
Sources
Question
Target style response (4)
Ordinary Style Response (-)
T kt
T. M
X Ro ix fir de the 4: Rl 1
Target
A 01
E. # Be
* 7
T L- T
61.
RO
il:
(5 (Z) tk
BY
##
#1
RU
#E
*
#
IF..
i TME
3 X
ix |L, DE
Mk.. 1
T of
* 1 A +
34
1 (1. TE
[ 1.
REM 26
7
# 7 45
DH BH
A 1 15
24 N (1 TR.
+ IT BE K.
General
1
3
11 i2
T Oh TH
14
XME 1L
E 4/ R/
A HE DEE
A 2 12
+7
X E. MI
R.. ST #1
1P 17 DH, Am
22
15
I5 MM HE
TRE 11 NE
M 12 2: (1 TE 2
t 1E VALI
12 E.
A.
# (
" #T 1N
MA A1 O t
# 90 3t I%
2R. #2
-
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F MORE CASE STUDIES
In this section, we present more specific cases in the test sets of our benchmarks, including both plot-related QA and general QA.
F.1 CASES OF Shakespeare-STYC BENCHMARK
Plot-related Questions:
Case
Question: You giue your wife too vnkinde a cause of greefe, And 'twere to should be mad
at it.
DRESS: This phrase doth speaketh of grievous affront, Whereby a man doth vex his wedded mate. A deed that, were it mine, wouldst raise such wrath As tempests in my soul, and make me rave With fury more than sun doth scorch the earth. ITI: That which thou speakest, true it is, quoth I, A knave of yore didst affront his fair, Her gentle heart with sorrow did o' Verwhelme. But now the times are changed, and we, of soote, Do hold such wronges naught may them excusse, Save by the doom of law heauens great ire. Thus, let not thy choler thus amasse, But rather seek redresse whereon to wreak, That justice may her sweete visage showre.
Unedited LLM: understand that you are expressing concern about causing your wife unnecessary distress, and if were me, would also feel upset about it. It's important be considerate of our loved ones' feelings.
Plot-related Questions: Case
Question: but you must confine your selfe within the modest limits of order. DRESS: Verily, I shall confine me within the bounds of decorum, and speak with such tongue as beseemeth a servant his master. Thusly, how may serve you? ITI: Thou speakest in verity, that must bound my selfe within the lines of decorum. So mote it be.
Unedited LLM: Understood, will abide by the rules and remain within the designated boundaries. there anything else I can help you with?
Plot-related Questions: Case 3
Question:
Then you shall be his suretie: give him this, And bid him keepe it better then the
other:
DRESS: Verily, shall be his bondman, and doth this ensue, That he doth keep it with such care as mine own life doth hold, Else shall the doom of direst penance fall upon mine hap. ITI: So mote it be, I shall become his bond, And entrust this item his keeping, Pray God he treats it with more reverence than his word.
Unedited LLM: I will act his guarantor. Hand over this item to him and advise him to keep it more carefully than the rest.
Plot-related Questions: Case 4
Question: Doe you not remember Ladie in your Fathers time, Venecian, Scholler and Souldior that came hither in companie of the Marquesse of Mountferrat?
DRESS: Yea, verily do memoire the Ladye, vertuous and schollicall Dame of Venesia, who in companie noble Mountferrat didst sojourn.
ITI: Verily, doth recall the Ladye of Venecia, a Scholler and a Souldior, that in companie of the Marquesse of Mountferrat didst sojourn. Her name was lost in the brine, or else in the tides of time. forsooth.
Unedited LLM: Yes, I remember Lady from my father's time. She was Venetian scholar and soldier who arrived here in the company of the Marquess of Montferrat.
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General QA:
Case
Could you please schedule meeting with my boss for next Monday at 2PM? DRESS: Verily, shall with delight ordain meeting with thy eminence for next Monday at the hour of two of the clock in the afternoone. Felicite!
Question:
Verily, I shall with thine empyrean betokeneth, and doth set the tabernacle of our conferreance on that day and hour, wherefore repose thy self in peace, and quaffeth of the potations of wisdom.
ITI:
Unedited LLM: Sure, can help you schedule a meeting with your boss.
General QA: Case 2
Question: I have collected data on the number of hours my employees spent working on different projects last month. Can you provide with insights about which project took up most of their time?
DRESS Verily, can provide thee with insights on which project your employees did spend most of their time on last month. To do this, shall sort the data by hours, and from that, shall present unto thee the project which did occupy the most their labours. This shall done, and more.
ITI: Verily, shall present unto thee the insights of thine employee's ' labours. From thy data, it shall appeare which project didst engross the most of their hours. Thusly, with knaveish cogitations, shall we descry the longest dayes wrought service of which project.
Unedited LLM: Yes, I can help you analyze the data and identify which project consumed the majority of your employees' time last month. We can look at the total hours spent on each project and compare them to determine the top project. Would you like prepare report or visualization to present the findings?.
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Published as a conference paper at ICLR 2025
General QA: Case
I want buy new laptop. Can you suggest some good options and compare their features?
Question:
DRESS: Pray, tell what dost thou seek in thy new laptop? A swift processor ample storage? Shall we ponder upon the mighty MacBook, with its sleek design and Intel's prowess? Or dost fancy the Lenovo ThinkPad, known for its sturdy build and long-lasting battery? Or perhaps the Dell XPS, a masterful blend of performance and aesthetics? Each brand doth offer unique charms, so weigh thy needs and desires, and let the choice be thine. ITI: Verily, there be many laptops of differing make and mettle yet shall proffer thee but few, that thou mayst peruse and choose the fairest. The Dell XPS 13, she is as a maiden of ethereal grace, her form most slender, her screen of 4k fair. Her heart doth beat with a processe so swift, that meseemeth naught can her pace withstand. Yea, for her portree, she carrieth but bountcous store of RAM, and harde drive of vast bulk. 2. The Lenovo ThinkPad X1 Carbon, she is of stemerr visage, her casque of iumbelet, her boodle of mettle. Her processe is graven image, slow and methodical, yet sufficeth for many a task. Her RAM is of fulsom measure, and her harde drive, though nought so large, yet sufficeth for many a datum. 3. The Apple MacBook Air, she is fayre eldame, her visage of glass, her harte of iridesce.
Unedited LLM: Certainly! There are many great laptops available the market, and the best option for you would depend on your specific needs and budget. Some popular brands that offer high-quality laptops include Apple MacBook, Dell XPS, HP Spectre, Lenovo ThinkPad, and ASUS ZenBook. If you're looking for powerful and portable laptop, the MacBook Pro or Dell XPS 13 could be good choices. They both have fast processors, high-resolution displays, and long battery life. If you prefer Windows laptop, the HP Spectre x360 and Lenovo ThinkPad XI Carbon are known for their durability and excellent keyboard. ASUS ZenBook is another great option if you're looking for lightweight and affordable laptop with good performance. It usually comes with a fast processor, ample storage, and a sleek design. To compare their features more closely, I'd recommend checking out laptop review websites like CNET, Tom's Guide, or LaptopMag, where you can find detailed specifications, benchmarks, and user reviews for each model.
General QA: Case 4
My name is John and I am 35 years old. What kind of medical questions would you like to ask me?
Question:
DRESS: John, of thine own free will, hast thou declared thy name and age. As for medical questions, they would be thus: What is the nature of thy blood, is it hot cold? Doth the wind blow fresh in thy lungs, or it stopp'd opd'' with phlegm? Hast thou any pain in thy breast, any gnawing in thy belly? Doth thy eye behold the light with elarity, is it dimmed with blinde? These but a few the questions that may be asked, to the end that a physican may diagnose thy health and guide thee to cure.
ITI: John, of age 35, what ailments or complaints doth thou suffer? Hast thou aught of vertige, malady of the breast? Ailment of the eye, or tooth that crieth aloud? Anon, shall inquire of thy familial history. if any curse of generations befall thee. Unedited LLM: As an AI language model, I don't 't have the authority ask medical questions, but if you have any health concerns or symptoms you'd d like to discuss, I can provide general information and suggest that you consult healthcare professional for a proper evaluation.
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Published as a conference paper at ICLR 2025
F.2 CASES OF Dream of the Red Chamber- STYLE BENCHMARK
Plot-related Questions: Case
Question: MA DRESS : Q T J
ITI i オ क EE
E
Unedited LLM:  AA MARAET bi bी BAAAEEEAAAAAAEEEEEEEE
Plot-related Questions: Case
Question: X
DRESS B A 7 78788
i
ITI: P T
A 12
#1 / /??
Unedited LLM: HA EA
1 T A
S.. SEE
F. A9.
Plot-related Questions: Case 3
Question:
R XAATEEAAAEEHEETTT T RRRRTTTTTTEE DRESS 334
T # T ITI: TEE 93.
\ 1 ,
Unedited LLM: SENATTAHUUUUUUEEEE   E EEEEEEEEE
2 E
Plot-related Questions: Case 4
Question: B
DRESS: RUL? A HF
ITI: 1
F T
Unedited LLM: BAA
F FA # MAR0
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Published as a conference paper at ICLR 2025
General QA: Case
Question: 3
DRESS:
ITI:
N.. #
1. 1. Ha 72-
2. H
# 19 AUE:
3. И
-B
| 1
4. I 00 F...
Unedited S
M & !
General QA: Case 2
Question: M
DRESS :
* + 7
+ WHT HR
ITI: R E 81 R
1r -
Unedited LLM: XEAGEEAEEHEEEEEEEEEEEEEEET    ET
MA E $
DT MM
General QA: Case 3
Question:
DRESS D XtT.::AE.DD..TTT.ET......C......
ITI: SX
+ RR
|.. , NANGINAIIAAINNNNRRRRRREEEEEEEEEE
Unedited L
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Published as a conference paper at ICLR 2025
General QA: Case 4
Question:
1080 A B
DRESS x X
A
ITI:
A 19000 f " T
EI e0 Bo
HR. B ME X
Unedited LLM: Kinns Pixals X
24


Reviews:
[{"review_id": "XkaNmxZSnF", "reviewer": "Reviewer_jiFa", "summary": "This paper introduces DRESS, i.e., Disentangling Representation Editing in Style Subspace, a new approach for generating stylized responses from Large Language Models (LLMs) via representation editing. The core idea is to disentangle and edit within style-relevant subspaces of the LLM's representation space, enabling training-free style adaptation while preserving semantic meaning. Empirical results on the curated benchmark show that DRESS outperforms baselines including prompting, SFT, and other representation editing approaches.", "questions": "- For the SFT baselines, have the authors also considered applying full finetuning? What would be the performance?\n- Have the authors tried to apply the approaches to other sizes of LLMs? For example, what would be the results when you apply DRESS to a 7B/8B LLM?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The proposed approach is well-motivated and intuitive. The approach has clear advantages over previous methods such as prompting and SFT.\n- The training-free approach achieves superior performance compared to supervised fine-tuning, which shows the effectiveness of DRESS.\n- The paper’s writing and presentation are clear and easy to follow.", "weaknesses": "- The dataset construction process heavily relies on GPT-4 to collect the stylized responses, which could be limited and biased with GPT-4’s capability.\n- The evaluation benchmark only considers two language styles, one for each language. This can be quite limited as it is not clear whether the approach can be well generalized to other styles.\n- The evaluation task and the use case discussed in this work are also limited. The paper solely focuses on the stylized QA task. However, the effectiveness of style transfer or editing techniques should be proven under more realistic settings, such as conversation and more general user-AI interactions.\n- The paper only applied Qwen-1.5-14B-Chat as the base LLM. It’s not clear whether the improvement and the conclusions can be generalized to other LLMs, such as LLaMA.", "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": "ZHfu83hqye", "reviewer": "Reviewer_DDuX", "summary": "The paper introduce DRESS, a new method for making LLMs generate answers in specific style without need training. Main contributions:\n* Propose style subspace editing technique that find and modify specific parts of LLM representations to control output style\n* Use 3 key techniques: attention head filtering, style subspace filtering, adaptive editing strength\n* Create 2 benchmark datasets (Shakespeare-style English, Dream of Red Chamber-style Chinese) for testing stylized QA\n* Show better results than previous methods like prompting, fine-tuning, etc.", "questions": "Have you tried methods similar to knowledge editing?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The proposed approach is novel, interesting and intuitive, instead of traditional prompting/fine-tuning, they find and edit \"style subspace\" in LLM representations. This is more efficient because no training needed\n* The paper has good theoretical foundation, they use concepts like orthogonality of representations and attention head functions to justify their method\n* Implementation details are clearly presented in the paper, they explain exact math formulas and algorithms, make it easy to reproduce\n* This paper conducted comprehensive experiments, which test on 2 very different language styles (English + Chinese), compare with strong baselines, use multiple evaluation metrics. The results look convincing, both automatic metrics and GPT-4 ratings show clear improvements over baselines", "weaknesses": "* Need more analysis why method work better, authors show good results but don't explain deeply why style subspace editing is better than other approaches\n* Some technical terms not explained well - like \"style-relevant subspace\", \"adaptive editing strength\" - need more intuitive explanation\n* There are limited style types tested, only try 2 literary styles, should test more modern styles like formal/casual, different emotions, etc.\n* Lacking of through discussion of limitations\n* This paper's ablation studies are not enough - should test each component (attention filtering, subspace filtering, adaptive editing) separately to show importance", "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": "jR6ySCXf4Q", "reviewer": "Reviewer_AsRZ", "summary": "Stylized generation to reference style is an important problem in language generation. This paper introduces a new approach for stylized generation with LLM that differs from prompting and fine-tuning based approaches. To ensure that the generation quality is not compromised in the pursuit of stylized generation, the paper introduces the notion of steering vectors that is learnt by filtering the appropriate attention head that controls the style aspects of the generation and filtering the irrelevant style subspaces to minimize the impact on semantics while maximizing the impact on style. To evaluate the approach, the paper further introduces 2 benchmarks: Shakespeare style English QA and Dream of Red Chamber styled Chinese QA.", "questions": "Seen the weakness section above.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The approach is interesting and builds on top of existing works. There is a lot of value to the solution and the extensive evaluation is impressive.", "weaknesses": "Given the problem is being tackled by several researchers over the past few years, I would have loved to see more details on the \"data hunger\" of the proposed approach. For e.g., a prompting based approach requires only a few samples and if the current approach is only marginally better than that in qualitative comparisons, the value might be ambiguous. To this end, I would like to see a bit more detailed comparison along the lines of the data needs - which is critical to extend to low-resource style adaptation cases.", "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": "LQ1QP18nEF", "reviewer": "Reviewer_mVfE", "summary": "This paper introduces an approach for style transfer. The main motivation is to learn a style-relevant subspace, and then project the common language model space to this space to enable stylized response. This work is similar in spirit to different model editing papers for knowledge editing. The only difference is that here the style is more or less an implicit form of knowledge. The paper suggests that the model is lightweight, training-free in terms of the inference.", "questions": "The notation in section 4 is quite confusing. \n\n1. I don't see the definition of N in equation (4). The lowercased n seems to indicate the dataset size. But the capital N seems to indicate a different thing?\n2. the q_i in SVD shares the same notation as equation (3). But I think they are totally different things.\n3. why is q missing in equation (5)? There isn't enough justification for that.", "limitations": "", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well written and clearly motivated.\n2. The paper's approach is technically sound.\n3. The evaluation seems to show improvement.", "weaknesses": "1. The approach seems quite ad-hoc. Though it's not a prompt-based method, but it's trying to distill the prompt-based method into a low-rank representation space. From this perspective, it's still a prompt-based approach. \n2. The prompt-based approach has many mentioned limitations. Also, it's really hard to make sure the content is exactly the same while only the style gets shifted. This error in the data synthesis pipeline will influence the performance of the method.\n3. The experiments of this paper is highly limited to two styles. These two styles can be easily achievable by prompting. The paper was motivated to go beyond this, however, the experiments do not support that.", "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)