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    <abstract>AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-paraphrased texts. However, AI paraphrasing is commonly employed in various application scenarios for text refinement and diversity. To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text. Different from text-level detection, PTD takes in the full text and assigns each of the sentences with a score indicating the paraphrasing degree. We construct a dedicated dataset, PASTED, for paraphrased text span detection. Both in-distribution and out-of-distribution results demonstrate the effectiveness of PTD models in identifying AI-paraphrased text spans. Statistical and model analysis explains the crucial role of the surrounding context of the paraphrased text spans. Extensive experiments show that PTD models can generalize to versatile paraphrasing prompts as well as multiple paraphrased text spans.</abstract>
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%0 Conference Proceedings
%T Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text
%A Li, Yafu
%A Wang, Zhilin
%A Cui, Leyang
%A Bi, Wei
%A Shi, Shuming
%A Zhang, Yue
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F li-etal-2024-spotting
%X AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-paraphrased texts. However, AI paraphrasing is commonly employed in various application scenarios for text refinement and diversity. To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text. Different from text-level detection, PTD takes in the full text and assigns each of the sentences with a score indicating the paraphrasing degree. We construct a dedicated dataset, PASTED, for paraphrased text span detection. Both in-distribution and out-of-distribution results demonstrate the effectiveness of PTD models in identifying AI-paraphrased text spans. Statistical and model analysis explains the crucial role of the surrounding context of the paraphrased text spans. Extensive experiments show that PTD models can generalize to versatile paraphrasing prompts as well as multiple paraphrased text spans.
%U https://aclanthology.org/2024.findings-acl.423
%P 7088-7107

Markdown (Informal)

[Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text](https://aclanthology.org/2024.findings-acl.423) (Li et al., Findings 2024)

• Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text (Li et al., Findings 2024)

ACL

• Yafu Li, Zhilin Wang, Leyang Cui, Wei Bi, Shuming Shi, and Yue Zhang. 2024. Spotting AI’s Touch: Identifying LLM-Paraphrased Spans in Text. In Findings of the Association for Computational Linguistics ACL 2024, pages 7088–7107, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.