Text Style Transfer Evaluation Using Large Language Models
This work addresses the challenge of reliable and reproducible evaluation in TST for researchers and practitioners, offering a more efficient method, though it is incremental as it builds on existing LLM capabilities.
The paper tackled the problem of evaluating Text Style Transfer (TST) by proposing the use of Large Language Models (LLMs) as an alternative to costly human evaluations and traditional automated metrics, finding that LLMs, especially with zero-shot prompting, show a strong correlation with human evaluations and often outperform existing metrics.
Evaluating Text Style Transfer (TST) is a complex task due to its multifaceted nature. The quality of the generated text is measured based on challenging factors, such as style transfer accuracy, content preservation, and overall fluency. While human evaluation is considered to be the gold standard in TST assessment, it is costly and often hard to reproduce. Therefore, automated metrics are prevalent in these domains. Nevertheless, it remains unclear whether these automated metrics correlate with human evaluations. Recent strides in Large Language Models (LLMs) have showcased their capacity to match and even exceed average human performance across diverse, unseen tasks. This suggests that LLMs could be a feasible alternative to human evaluation and other automated metrics in TST evaluation. We compare the results of different LLMs in TST using multiple input prompts. Our findings highlight a strong correlation between (even zero-shot) prompting and human evaluation, showing that LLMs often outperform traditional automated metrics. Furthermore, we introduce the concept of prompt ensembling, demonstrating its ability to enhance the robustness of TST evaluation. This research contributes to the ongoing evaluation of LLMs in diverse tasks, offering insights into successful outcomes and areas of limitation.