# Learning Interpretable Style Embeddings via Prompting LLMs

**Authors:** Ajay Patel, Delip Rao, Chris Callison-Burch  
**arXiv:** [2305.12696](https://arxiv.org/abs/2305.12696)  
**Date:** 2023-05-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.12696v1/

## Key points

- The paper discusses style representation learning in text
- Stylometry is typically conducted by expert forensic linguists
- Lack of large datasets with stylometric annotations for training purposes
- Current methods utilize neural techniques to create style vectors
- Uninterpretable representations hinder effective use in downstream applications
- Authors propose a novel approach using prompting to perform stylometry on texts
- Synthetic dataset generated for training human-interpretable style representations (LISA embeddings)
- Aim to provide interpretable models that can be audited and understood easily
- Release synthetic stylometry dataset and interpretable style models as resources
- Contributes to advancing the field of style representation learning

## Summary

The paper titled "Learning Interpretable Style Embeddings via Prompting LLMs" discusses the field of style representation learning, which aims to develop content-independent representations of author style in text. Stylometry, the analysis of style in text, is typically conducted by expert forensic linguists; however, there is a lack of large datasets with stylometric annotations for training purposes. Current methods in style representation learning utilize neural techniques to disentangle style from content and create style vectors. However, these approaches often result in uninterpretable representations, making it challenging to use them effectively in downstream applications such as authorship attribution where auditing and explainability are crucial. To address this issue, the authors propose a novel approach using prompting to perform stylometry on a significant number of texts. This allows them to generate a synthetic dataset that can be used for training human-interpretable style representations called LISA embeddings. By leveraging this approach, the authors aim to provide interpretable models that can be easily audited and understood. In addition to their proposed methodology, the authors also release their synthetic stylometry dataset and interpretable style models as valuable resources for further research and development in the field. Overall, this work contributes significantly to advancing the field of style representation learning by providing an interpretable framework for analyzing author styles in text data which can be easily audited and understood. It also provides researchers with valuable resources such as a synthetic stylometry dataset and interpretable style models that can be used for further research and development in the area.