Multidimensional Evaluation for Text Style Transfer Using ChatGPT
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Abstract (arXiv)
We investigate the potential of ChatGPT as a multidimensional evaluator for the task of 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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 34 of its 44 references that are in the map. First posted on arXiv 26 Apr 2023.
Builds on
34 of 44 references.
References · 34
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22 more
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Citing papers per year: 2023: 3; 2024: 6; 2025: 2; 2026: 1.
12 papers.
- 2024CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfercited by 10
- 2023Learning Evaluation Models from Large Language Models for Sequence Generationcited by 6
- 2023VQA-GEN: A Visual Question Answering Benchmark for Domain Generalizationcited by 5
- 2024LMStyle Benchmark: Evaluating Text Style Transfer for Chatbotscited by 5
- 2025Evaluating Style-Personalized Text Generation: Challenges and Directionscited by 3
- 2024T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Setscited by 2
- 2024Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcriptscited by 2
- 2025Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metricscited by 2
- 2023PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfercited by 0
- 2024Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?cited by 0
- 2024FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMscited by 0
- 2026Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generationcited by 0
- 2026Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation3 Sep 2026
- 2025Evaluating Style-Personalized Text Generation: Challenges and Directions8 Aug 2025
- 2025Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics20 Feb 2025
- 2024Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts19 Aug 2024
- 2024FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs9 Aug 2024
- 2024Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?7 Aug 2024
- 2024T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Sets24 Jul 2024
- 2024LMStyle Benchmark: Evaluating Text Style Transfer for Chatbots13 Mar 2024
- 2024CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfer11 Jan 2024
- 2023PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer14 Nov 2023
- 2023VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization1 Nov 2023
- 2023Learning Evaluation Models from Large Language Models for Sequence Generation8 Aug 2023