@inproceedings{patel-etal-2023-learning,
title = "Learning Interpretable Style Embeddings via Prompting {LLM}s",
author = "Patel, Ajay and
Rao, Delip and
Kothary, Ansh and
McKeown, Kathleen and
Callison-Burch, Chris",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.1020",
doi = "10.18653/v1/2023.findings-emnlp.1020",
pages = "15270--15290",
abstract = "Style representation learning builds content-independent representations of author style in text. To date, no large dataset of texts with stylometric annotations on a wide range of style dimensions has been compiled, perhaps because the linguistic expertise to perform such annotation would be prohibitively expensive. Therefore, current style representation approaches make use of unsupervised neural methods to disentangle style from content to create style vectors. These approaches, however, result in uninterpretable representations, complicating their usage in downstream applications like authorship attribution where auditing and explainability is critical. In this work, we use prompting to perform stylometry on a large number of texts to generate a synthetic stylometry dataset. We use this synthetic data to then train human-interpretable style representations we call LISA embeddings. We release our synthetic dataset (StyleGenome) and our interpretable style embedding model (LISA) as resources.",
}
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<abstract>Style representation learning builds content-independent representations of author style in text. To date, no large dataset of texts with stylometric annotations on a wide range of style dimensions has been compiled, perhaps because the linguistic expertise to perform such annotation would be prohibitively expensive. Therefore, current style representation approaches make use of unsupervised neural methods to disentangle style from content to create style vectors. These approaches, however, result in uninterpretable representations, complicating their usage in downstream applications like authorship attribution where auditing and explainability is critical. In this work, we use prompting to perform stylometry on a large number of texts to generate a synthetic stylometry dataset. We use this synthetic data to then train human-interpretable style representations we call LISA embeddings. We release our synthetic dataset (StyleGenome) and our interpretable style embedding model (LISA) as resources.</abstract>
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%0 Conference Proceedings
%T Learning Interpretable Style Embeddings via Prompting LLMs
%A Patel, Ajay
%A Rao, Delip
%A Kothary, Ansh
%A McKeown, Kathleen
%A Callison-Burch, Chris
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F patel-etal-2023-learning
%X Style representation learning builds content-independent representations of author style in text. To date, no large dataset of texts with stylometric annotations on a wide range of style dimensions has been compiled, perhaps because the linguistic expertise to perform such annotation would be prohibitively expensive. Therefore, current style representation approaches make use of unsupervised neural methods to disentangle style from content to create style vectors. These approaches, however, result in uninterpretable representations, complicating their usage in downstream applications like authorship attribution where auditing and explainability is critical. In this work, we use prompting to perform stylometry on a large number of texts to generate a synthetic stylometry dataset. We use this synthetic data to then train human-interpretable style representations we call LISA embeddings. We release our synthetic dataset (StyleGenome) and our interpretable style embedding model (LISA) as resources.
%R 10.18653/v1/2023.findings-emnlp.1020
%U https://aclanthology.org/2023.findings-emnlp.1020
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1020
%P 15270-15290
Markdown (Informal)
[Learning Interpretable Style Embeddings via Prompting LLMs](https://aclanthology.org/2023.findings-emnlp.1020) (Patel et al., Findings 2023)
ACL
- Ajay Patel, Delip Rao, Ansh Kothary, Kathleen McKeown, and Chris Callison-Burch. 2023. Learning Interpretable Style Embeddings via Prompting LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15270–15290, Singapore. Association for Computational Linguistics.