# CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfer

arXiv: https://arxiv.org/abs/2401.05707 · HTML: https://mlsanity.com/paper/2401.05707/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4](https://mlsanity.com/paper/2311.07361/) (2023)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://mlsanity.com/paper/1905.05621/) (2019)
- [Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts](https://mlsanity.com/paper/2209.12711/) (2022)
- [ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation](https://mlsanity.com/paper/2307.15290/) (2023)
- [Adapting Language Models for Non-Parallel Author-Stylized Rewriting](https://mlsanity.com/paper/1909.09962/) (2019)
- [UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation](https://mlsanity.com/paper/2311.15296/) (2023)
- [Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization](https://mlsanity.com/paper/2108.00449/) (2021)
- [A Review of Text Style Transfer using Deep Learning](https://mlsanity.com/paper/2109.15144/) (2021)
- [Stylized Dialogue Response Generation Using Stylized Unpaired Texts](https://mlsanity.com/paper/2009.12719/) (2020)
- [Multidimensional Evaluation for Text Style Transfer Using ChatGPT](https://mlsanity.com/paper/2304.13462/) (2023)
- [StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing](https://mlsanity.com/paper/2208.13423/) (2022)
- [DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting](https://mlsanity.com/paper/2101.11836/) (2021)

## Built on by (most cited first, 10 of 10)

- [Bringing Generative AI to Adaptive Learning in Education](https://mlsanity.com/paper/2402.14601/) (2024)
- [Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs](https://mlsanity.com/paper/2402.11218/) (2024)
- [Creative and Context-Aware Translation of East Asian Idioms with GPT-4](https://mlsanity.com/paper/2410.00988/) (2024)
- [Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions](https://mlsanity.com/paper/2402.13647/) (2024)
- [Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts](https://mlsanity.com/paper/2408.09688/) (2024)
- [Towards Conditioning Clinical Text Generation for User Control](https://mlsanity.com/paper/2502.17571/) (2025)
- [Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping](https://mlsanity.com/paper/2505.07888/) (2025)
- [FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs](https://mlsanity.com/paper/2408.05365/) (2024)
- [Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs](https://mlsanity.com/paper/2412.20440/) (2024)
- [In-Place Instruction Following in Diffusion Language Models](https://mlsanity.com/paper/2609.07160/) (2026)

## Abstract (arXiv)

Text style transfer plays a vital role in online entertainment and social media. However, existing models struggle to handle the complexity of Chinese long texts, such as rhetoric, structure, and culture, which restricts their broader application. To bridge this gap, we propose a Chinese Article-style Transfer (CAT-LLM) framework, which addresses the challenges of style transfer in complex Chinese long texts. At its core, CAT-LLM features a bespoke pluggable Text Style Definition (TSD) module that integrates machine learning algorithms to analyze and model article styles at both word and sentence levels. This module acts as a bridge, enabling LLMs to better understand and adapt to the complexities of Chinese article styles. Furthermore, it supports the dynamic expansion of internal style trees, enabling the framework to seamlessly incorporate new and diverse style definitions, enhancing adaptability and scalability for future research and applications. Additionally, to facilitate robust evaluation, we created ten parallel datasets using a combination of ChatGPT and various Chinese texts, each corresponding to distinct writing styles, significantly improving the accuracy of the model evaluation and establishing a novel paradigm for text style transfer research. Extensive experimental results demonstrate that CAT-LLM, combined with GPT-3.5-Turbo, achieves state-of-the-art performance, with a transfer accuracy F1 score of 79.36% and a content preservation F1 score of 96.47% on the "Fortress Besieged" dataset. These results highlight CAT-LLM's innovative contributions to style transfer research, including its ability to preserve content integrity while achieving precise and flexible style transfer across diverse Chinese text domains. Building on these contributions, CAT-LLM presents significant potential for advancing Chinese digital media and facilitating automated content creation.