Multidimensional Evaluation for Text Style Transfer Using ChatGPT
This work addresses the need for better evaluation methods in text style transfer, though it is incremental as it applies an existing model to a new task.
The study explored ChatGPT's ability to evaluate text style transfer across style strength, content preservation, and fluency, finding it achieved competitive correlations with human judgments compared to existing automatic metrics.
We investigate the potential of ChatGPT as a multidimensional evaluator for the task of \emph{Text Style Transfer}, alongside, and in comparison to, existing automatic metrics as well as human judgements. We focus on a zero-shot setting, i.e. prompting ChatGPT with specific task instructions, and test its performance on three commonly-used dimensions of text style transfer evaluation: style strength, content preservation, and fluency. We perform a comprehensive correlation analysis for two transfer directions (and overall) at different levels. Compared to existing automatic metrics, ChatGPT achieves competitive correlations with human judgments. These preliminary results are expected to provide a first glimpse into the role of large language models in the multidimensional evaluation of stylized text generation.