A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025)

Abstract: Text style transfer (TST) modifies a source sentence to match a target style while preserving its semantics. While existing models perform well on simple styles like sentiment and formality, they struggle with complex, entangled styles such as poetry and brand-specific tones, which require advanced operations to disentangle content and style. We propose a multi-agent self-check framework that contains a large language model (LLM) as a planner for disentangling subtasks and expert agents for executing the subtasks. This training-free multi-agent framework decomposes TST into manageable components, enabling iterative refinement through a self-check module that balances style adherence and content preservation. Experiments on both simple and complex style datasets show our framework significantly improves style strength and content preservation, with strong adaptability in few-shot settings.

## introduction the text style transfer (tst) task aims to modify a source sentence to match a target style while preserving its original semantics. this task is essential for making nlp applications more user-centered and is widely applied in areas such as dialogue systems @xcite @xcite @xcite , writing assistants @xcite ), text debiasing @xcite @xcite , and online healthcare systems @xcite . tst models can handle a diverse range of styles, such as sentiment @xcite , formality @xcite , shakespearean @xcite , and beyond. existing approaches @xcite @xcite have achieved promising results on simple style transfer benchmarks such as sentiment @xcite , where stylistic attributes are well-defined and can be modified with minimal fixed edits. however, these methods struggle with complex styles-such as poetry, biblical language, or brand-specific tones-which require broader lexical and structural transformations, necessitating more flexible and adaptive edits, as shown in figure 3 . such style transfer is particularly challenging due to the entanglement of style and content within sentences: a single token often carries both semantic and stylistic meaning, so applying fixed edits focused solely on style can result in the loss of essential information from the original sentence. several efforts have been made to tackle complex style transfer. some traditional methods @xcite rely on predefined stylistic rules or attribute templates, but these approaches require extensive manual effort and are difficult to generalize. meanwhile, some llm-based methods @xcite @xcite devise specific loss functions to control the output, and the most recent work @xcite leverages disentangled data generation to enhance the training process. nonetheless, these methods rely on single-step generation, which limits their ability to balance style strength and content preservation. when style and content are entangled, enforcing a strong stylistic transformation in a single step often results in semantic drift, as the model lacks the capacity to iteratively adjust and refine the output. we suggest that decomposition can help address these limitations by breaking the complex style transfer task into a sequence of simpler subtasks. some subtasks focus on stylistic transformation, while others concentrate on content preservation. this separation enables more fine-grained control and reduces interference between style and meaning. however, applying decomposition to text style transfer raises two key questions: (1) how can we automatically decompose style and content for a new target style without strong supervision signals? and (2) how can we coordinate the subtasks to avoid conflicts and maintain a balance between style strength and content preservation? to address these challenges, we propose a multiagent self-check framework that decomposes the style transfer task into subtasks coordinated by a large language model (llm) planner and executed by multiple expert agents. the training-free planner automatically generates both a subtask plan and an interaction plan, specifying the roles of individual agents and how they communicate. each subtask is handled by an llm acting as an expert agent. these agents are interconnected based on the planner's instructions and collaborate through a self-check module, which monitors their outputs for consistency. this module iteratively evaluates content preservation while ensuring that stylistic goals are met, reducing conflicts between agents and improving the overall coherence of the output. our experiments cover both simple tst datasets (yelp @xcite and gyafc @xcite ) and complex, entangled style datasets (cds @xcite and shakespeare @xcite ). leveraging a multi-agent self-check strategy powered by llms, our framework achieves substantial improvements in both style strength and content preservation. specifically, on the most challenging cds dataset, we observe a 2.8% increase in style strength and a 4.4% improvement in content preservation. furthermore, as a training-free method, our framework shows strong adaptability to diverse styles, even in challenging few-shot scenarios. in summary, our contributions are as follows: ## related work traditional methods. traditional approaches to text style transfer have primarily focused on simple style transfer tasks, some approaches @xcite @xcite propose style-oriented losses, and some approaches @xcite @xcite leverage an attribute classifier on representations. recently, styletrans @xcite , racoln @xcite , and dirr @xcite have achieve promising result on sentiment transfer. styletrans employs style embeddings and incorporates three specific loss functions to provide supervision signals for effective style transformation. dirr adopts a reinforcement learning approach, using a semantic similarity metric as a reward to preserve content during training. racoln leverages a reverse attention mechanism to implicitly remove style tokens while integrating content information into style representations through conditional layer normalization. however, it is important to note that these traditional methods depend on labeled training data for model development, limiting their adaptability. our comparisons with these methods primarily focus on tasks involving simple style transfer. generation based methods with llm. in contrast, llm-based methods have begun to leverage llms to achieve competitive performance on complex style transfer tasks @xcite . for instance, disentrans @xcite introduces a disentangled cot prompting mechanism to synthesize parallel data along with corresponding attribute components for supervision. the model design two custom loss functions to enhance attention to attribute properties and constrain the semantic space, resulting in improved performance on more intricate style transfer tasks @xcite . however, despite leveraging the concept of disentanglement, it employs a single-step generation process, which can result in conflicts between style strength and content preservation during generation. ## preliminary given a set of styles @xmath0, s 2 , ..., s n } and a corpus @xmath1, s i )}, where x ij is a natural sentence and si is its corresponding style label. the text style transfer task is to acquire a model m which takes a natural language sentence x along with a desired style @xmath2, and then generates a new sentence x ′ that aligns with the desired style s while maintaining the semantic information of the original input sentence x. to be noted, our proposed framework is training-free, requiring only a small corpus d with some example sentences for the target style, therefore, no parallel training data is needed in our framework. ## approach we introduce a multi-agent self-check framework designed for entangled text style transfer tasks. the framework employs an llm as a planner (section 3.1) to generate style transfer plans and assigns expert llm agents (section 3.2) to execute each subtask, all powered by gpt-3.5. as illustrated in figure 2 , given an input sentence and a target style, the planner produces both subtask and interaction plans, decomposing the entangled style transfer into simpler, manageable steps. each subtask agent operates sequentially according to the plan and is interconnected through a self-check module, which ensures consistency and prevents conflicts across subtasks. this process is recursive, as the framework iteratively coordinates the agents to address each subtask, ensuring that the final out-you will be provided with a style name and a set of example sentences in that style. your task is to analyze the examples and create an abstract plan within 5 steps for transforming a given sentence into the specified style. the plan should outline key stylistic characteristics, such as tone, vocabulary, sentence structure, and any specific patterns or rules observed in the examples. following the examples, the plan should be a general guideline for style transfer. put adheres to the target style while preserving the original meaning. ## experiment in this section, we present the empirical evaluation of our proposed multi-agent framework. we begin by introducing the evaluation metrics, datasets, baselines, and experimental setups. next, we present the main results, followed by a detailed ablation analysis. ## conclusion we propose a multi-agent self-check framework for text style transfer, using an llm planner and expert agents for subtasks. unlike prior models that struggle with complex styles, our training-free approach decomposes the task and enables iterative refinement through self-checking, balancing style and content. experiments on both simple and complex datasets demonstrate that our framework achieves improvements in both style strength and content preservation. moreover, our approach showcases strong adaptability in few-shot settings, underscoring its potential as a robust and efficient solution for diverse tst tasks.