LMStyle Benchmark: Evaluating Text Style Transfer for Chatbots
This work addresses the problem of evaluating style transfer for conversational models, providing a scalable framework for researchers and developers, though it is incremental as it builds on existing style transfer concepts.
The paper tackles the lack of standard evaluation metrics for text style transfer in chatbots by proposing the LMStyle Benchmark, which introduces appropriateness metrics and shows higher correlation with human judgments.
Since the breakthrough of ChatGPT, large language models (LLMs) have garnered significant attention in the research community. With the development of LLMs, the question of text style transfer for conversational models has emerged as a natural extension, where chatbots may possess their own styles or even characters. However, standard evaluation metrics have not yet been established for this new settings. This paper aims to address this issue by proposing the LMStyle Benchmark, a novel evaluation framework applicable to chat-style text style transfer (C-TST), that can measure the quality of style transfer for LLMs in an automated and scalable manner. In addition to conventional style strength metrics, LMStyle Benchmark further considers a novel aspect of metrics called appropriateness, a high-level metrics take account of coherence, fluency and other implicit factors without the aid of reference samples. Our experiments demonstrate that the new evaluation methods introduced by LMStyle Benchmark have a higher correlation with human judgments in terms of appropriateness. Based on LMStyle Benchmark, we present a comprehensive list of evaluation results for popular LLMs, including LLaMA, Alpaca, and Vicuna, reflecting their stylistic properties, such as formality and sentiment strength, along with their appropriateness.