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Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity

arXiv 2410.17670 ↗First posted 23 Oct 2024cs.CLcited by 0

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Abstract (arXiv)

Writing assistants and large language models see widespread use in the creation of text content. While their effectiveness for individual users has been evaluated in the literature, little is known about their proclivity to change language or reduce its richness when adopted by a large user base. In this paper, we take a first step towards quantifying this risk by measuring the semantic and vocabulary change enacted by the use of rephrasing tools on a multi-domain corpus of human-generated text.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 23 Oct 2024.

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References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  4. 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 98
  5. 2021Sparks: Inspiration for Science Writing using Language Modelscited by 80
  6. 2024Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Studycited by 10

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