Text Style Transfer Evaluation Using Large Language Models

Text Style Transfer (TST) is challenging to evaluate because the quality of the generated text manifests itself in multiple aspects, each of which is hard to measure individually: style transfer accuracy, content preservation, and overall fluency of the text. Human evaluation is the gold standard in TST evaluation; however, it is expensive, and the results are difficult to reproduce. Numerous automated metrics are employed to assess performance in these aspects, serving as substitutes for human evaluation. However, the correlation between many of these automated metrics and human evaluations remains unclear, raising doubts about their effectiveness as reliable benchmarks. Recent advancements in Large Language Models (LLMs) have demonstrated their ability to not only match but also surpass the average human performance across a wide range of unseen tasks. This suggests that LLMs have the potential to serve as a viable alternative to human evaluation and other automated metrics. We assess the performance of different LLMs on TST evaluation by employing multiple input prompts and comparing their results. Our findings indicate that (even zero-shot) prompting correlates strongly with human evaluation and often surpasses the performance of (other) automated metrics. Additionally, we propose the ensembling of prompts and show it increases the robustness of TST evaluation.This work contributes to the ongoing efforts in evaluating LLMs on diverse tasks, which includes a discussion of failure cases and limitations.

A Call for Standardization and Validation of Text Style Transfer Evaluation

Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly examines existing literature in the field. The meta-analysis reveals a substantial standardization gap in human and automated evaluation. In addition, we also find a validation gap: only few automated metrics have been validated using human experiments. To this end, we thoroughly scrutinize both the standardization and validation gap and reveal the resulting pitfalls. This work also paves the way to close the standardization and validation gap in TST evaluation by calling out requirements to be met by future research.