PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

• URL: http://arxiv.org/abs/2311.08389v3
• Date: Mon, 14 Oct 2024 03:00:10 GMT
• Title: PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
• Authors: Huashan Sun, Yixiao Wu, Yuhao Ye, Yizhe Yang, Yinghao Li, Jiawei Li, Yang Gao,
• Abstract summary: We introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles. We propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts.
• Score: 16.07576878783396
• License:
• Abstract: Language style is necessary for AI systems to understand and generate diverse human language accurately. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application needs. To overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking style. Grounded in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of LLMs. For such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized texts. Comprehensive experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information.

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