Challenge: Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.
Approach: They propose a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters.
Outcome: The proposed models outperform models that encapsulate single-style or average-style language generation capabilities.

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Challenge: Existing methods for text style transfer require style-labeled training data, but use only labeled data at inference time.
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Style Vectors for Steering Generative Large Language Models (2024.findings-eacl)

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Challenge: Large language models (LLMs) can be trained on vast corpora and can generate text in a nuanced and parameterisable way.
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Prefix-Tuning Based Unsupervised Text Style Transfer (2023.findings-emnlp)

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Challenge: Unsupervised text style transfer is an important task in computer vision and natural language processing.
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Towards Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)

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Challenge: a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator .
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Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding (2024.acl-long)

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Challenge: Existing large language models struggle to capture some language styles without fine-tuning.
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Text Generation with Exemplar-based Adaptive Decoding (N19-1)

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Challenge: Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines.
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MPCoder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning (2024.acl-long)

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Challenge: Existing Large Language Models are usually generalized with large programming corpus, therefore the generated code is difficult to adapt to personalized and/or customized requests.
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Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer (2022.naacl-main)

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Challenge: Recent studies show that auto-encoders perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner.
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Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)

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Challenge: State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages.
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How Positive Are You: Text Style Transfer using Adaptive Style Embedding (2020.coling-main)

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Challenge: Existing approaches for unsupervised text style transfer are disentanglement between content and style.
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