Interpreting Style Representations via Style-Eliciting Prompts

Conference: ACL2026
arXiv: 2606.05716
Code: https://github.com/junghwanjkim/style-decoding
Area: Interpretability / Style Control
Keywords: Style representation, style prompts, interpretable representation, text style control, synthetic supervision

TL;DR

This paper decodes difficult-to-interpret text style vectors into style-eliciting prompts that can directly drive LLM writing. Using "controllability" as the interpretability standard, the method outperforms baselines that directly use LLMs to describe target text styles in tasks such as style recovery, synthetic text style control, and human text style imitation.

Background & Motivation

Background: Style representation models can already map text into vector spaces representing writing styles for tasks like authorship verification, style comparison, and style transfer. These vectors are typically trained via contrastive learning and capture multi-layered stylistic signals including vocabulary, syntax, tone, and rhetoric.

Limitations of Prior Work: Style vectors are effective but opaque. Existing interpretation methods often require an LLM to read a text segment and generate a natural language style description. However, such descriptions are susceptible to LLM priors and hallucinations and often remain purely explanatory text that cannot reliably reproduce the target style.

Key Challenge: A good style explanation should not only "sound similar" but also be "usable." If a description fails to guide an LLM to generate text in the same style, its value for interpreting style representations is limited.

Goal: The authors aim to convert latent style representations into natural language style prompts. These prompts should be human-readable while serving as control instructions to prompt an LLM to generate new text with matching stylistic characteristics.

Key Insight: The paper constructs supervision data in reverse: it first designs explicit style prompts, then tasks an LLM to generate text based on them. Since the "true stylistic intent" of the generated text is known, a decoder can be trained to recover the original style prompt from the text's style vector.

Core Idea: Use synthetic prompt-text pairs for supervised training of a style decoder, transforming the interpretation problem into prompt recovery and validating the interpretability through the stylistic distance of the resulting generated text.

Method

The problem addressed is: given a vector \(x\) produced by a style representation model \(S\), learn a decoder \(D\) that outputs a natural language style prompt \(s\), such that an LLM generating new text \(y\) under that prompt produces a style vector \(S(y)\) close to the original \(x\). Since directly searching the discrete prompt space is infeasible, the authors construct synthetic supervision to recover known prompts from the style vectors of synthetic text.

Overall Architecture

Data construction consists of three steps. First, 1,010 specific style features across 26 categories (e.g., sentence structure, tone, formality, descriptive density, abstraction level) were generated via GPT-4o and manually cleaned. Second, 300,000 real-world QA pairs were sampled from Reddit, StackExchange, and Yahoo Answers, with original human answers preserved for human style evaluation. Third, 1 to 10 style features were randomly combined to form style prompts, and Phi-4, Qwen2.5-14B, and OLMo-2-13B were used to generate stylized responses, resulting in 1.8M LLM responses and 434,535 unique style prompts.

The model architecture consists of a frozen style representation model, a trainable projection module, and a frozen LLM decoder. The style representation model uses Mistral-Nemo-Instruct-2407, trained via contrastive learning on author-labeled data. The projection module is a three-layer feedforward network that projects the style vector into 20 continuous token embeddings. These embeddings, together with natural language instructions, are input into Ministral-8B-Instruct to generate a style prompt in the form of "The author uses ...".

Key Designs

1. Generating text from prompts rather than describing text:

   • Function: Establishes a verifiable ground-truth prompt for style interpretation.
   • Mechanism: Specific style features are sampled to form a prompt, and the LLM generates a response. During training, the decoder recovers the prompt from the generated text's style representation.
   • Design Motivation: Direct LLM descriptions of existing text often involve hallucinations or omissions; text generated from known prompts provides explicit supervision signals.

2. Continuous prompts to bridge style vectors to frozen LLMs:

   • Function: Converts dense style vectors into natural language descriptions without fine-tuning the base LLM.
   • Mechanism: A three-layer MLP maps the style vector to 20 token embeddings serving as a continuous prefix. The frozen LLM generates the style prompt based on this prefix and task instructions.
   • Design Motivation: Style representations are continuous vectors while LLM generation is discrete text; continuous prompt tuning provides a lightweight bridging layer.

3. Evaluating interpretation quality via control effectiveness:

   • Function: Validates whether decoded prompts can truly reproduce the target style.
   • Mechanism: Beyond ROUGE-1, LaBSE, and LLM-as-judge for prompt recovery, decoded prompts are used to generate new responses. The L2 distance between the new text and the target text in the style representation space is then measured.
   • Design Motivation: A style explanation is merely descriptive if it cannot guide generation; incorporating control efficacy into evaluation directly tests the operational utility of the explanation.

Loss & Training

The training objective is token-level cross-entropy, matching the decoder-generated \(\tilde{s}=D(S(x))\) to the ground-truth style prompt \(s\). Data is split 8:1:1 for training, validation, and testing. The decoder is trained for 5 epochs with a learning rate of 5e-5 and a batch size of 32. The optimal checkpoint is selected based on validation loss. All Section 6/7 results use a test set of 180K LLM responses, while Section 8 uses 60K human responses. Training utilized PyTorch-Lightning, HuggingFace Transformers, AdamW, and a WSD learning rate schedule, taking approximately 16 hours on 2 A100 GPUs.

Key Experimental Results

Main Results

Scenario	Method	Our Embedding L2↓	LUAR L2↓	StyleDistance L2↓
LLM-generated style control	Decoder (Ours)	26.07	6.01	6.82
LLM-generated style control	LLM Custom	35.39	9.10	8.24
LLM-generated style control	Wang et al. 2025	73.21	8.26	8.41
LLM-generated style control	Jangra et al. 2025	100.10	8.90	9.85
LLM-generated style control	Bhandarkar et al. 2024	102.89	9.02	11.87
LLM-generated style control	TinyStyler	49.97	11.40	10.82
Human style steering	Decoder (Ours)	27.73	6.33	7.47
Human style steering	LLM Custom	37.54	9.39	9.79
Human style steering	Bhandarkar et al. 2024	35.53	9.31	8.94
Human style steering	TinyStyler	54.69	11.77	14.38

Lower L2 distance indicates the generated style is closer to the target. Whether evaluated using the style embedding from training or unseen representations like LUAR and StyleDistance, the proposed decoder achieves the lowest distance, indicating it does not merely overfit a single representation space.

Ablation Study

Component/Data	Value or Setting	Description
Style Features	1,010	Covering 26 stylistic categories
QA Pairs	300,000	From Reddit, StackExchange, Yahoo Answers
Synthetic Responses	1.8M	Generated by Phi-4, Qwen2.5-14B, OLMo-2-13B
Unique Prompts	434,535	1-10 style features per prompt combination
Human Responses	300K	Used for real human writing style steering evaluation
Projection Output	20 token embeddings	Interfaces style vector with frozen LLM
Decoder LLM	Ministral-8B-Instruct	Frozen backbone, only projection layer trained

Key Findings

• In the prompt recovery task, the proposed method yields improvements of 76.0% in ROUGE-1, 21.7% in LaBSE, and 42.8% in LLM-as-judge scores compared to baselines.
• In style control tasks, the method achieves a 12.9% L2 improvement on LLM-generated references and 26.1% on human-written references relative to baselines.
• LLM-based style description baselines perform worse than a random prompt baseline in prompt recovery, suggesting that "describing style after reading text" is not equivalent to recovering the actual stylistic intent that drove the text's generation.
• t-SNE visualizations show that different style prompts form distinct clusters, and semantically similar styles are closer in representation space, supporting the premise that style representations contain decodable stylistic information.

Highlights & Insights

• The integration of interpretability and controllability is the most insightful aspect. A style prompt serves not just as a human-readable label but also as a control interface for text generation.
• The synthetic supervision design is clever: while it is difficult to determine true style labels for real text, generating text from prompts provides clear, fine-grained, and compositional supervision signals.
• Evaluation across multiple style representations—including LUAR and StyleDistance which were not used during training—alleviates concerns about the method being effective only on its own embedding space.

Limitations & Future Work

• The authors acknowledge the method is primarily focused on English. Stylistic dimensions, syntactic expressions, and LLM/style representation quality differ across languages; cross-lingual generalization cannot be assumed.
• The data domain is limited to online Q&A. Further evaluation is needed to determine if the model generalizes to fiction, formal documents, academic writing, news, or legal texts.
• Synthetic data relies on the LLM's instruction-following capabilities. If the LLM executes subtle stylistic features inconsistently, the decoder might learn LLM-specific stylistic biases rather than general human writing styles.
• The current decoder outputs prompt-level explanations but has not demonstrated how specific lexical or syntactic phenomena are encoded in the style vector; more fine-grained attribution or disentanglement remains a future direction.

Related Work & Insights

• vs LLM style description: Directly prompting LLMs to describe target styles is influenced by content and model bias; this work decodes from the style vector and uses ground-truth style prompt supervision for interpretations closer to the representation itself.
• vs style transfer: Style transfer usually requires preserving input content while changing style; this work does not require content preservation but rather focuses on explaining and reproducing style, making it more suitable for analyzing latent style representations.
• vs prompt discovery: Traditional prompt discovery targets specific outputs or behaviors; this work focuses on inducing specific writing styles through synthetic supervision rather than RL searches.

Rating

• Novelty: ⭐⭐⭐⭐⭐ The use of style-eliciting prompts to explain style vectors and the validation via control effectiveness is a compelling setup.
• Experimental Thoroughness: ⭐⭐⭐⭐☆ Covers three tasks, multiple baselines, various style representations, and human text evaluation; however, it lacks cross-lingual and cross-genre testing.
• Writing Quality: ⭐⭐⭐⭐☆ Motivations, data construction, and model structures are clear; some values in the main figures require reference to the appendix.
• Value: ⭐⭐⭐⭐☆ Offers direct insights for interpretable style modeling, personalized writing assistants, persona simulation, and controllable generation.

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