Understanding the effects of human-written paraphrases in LLM-generated text detection
Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be aware when a piece of text is LLM-generated. This has brought about the need for bui...
Saved in:
| Main Authors: | Hiu Ting Lau, Arkaitz Zubiaga |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Natural Language Processing Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949719125000275 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SMCLM: Semantically Meaningful Causal Language Modeling for Autoregressive Paraphrase Generation
by: Michal Perelkiewicz, et al.
Published: (2025-01-01) -
TriPlaNet: Enhancing machine-paraphrasing plagiarism detection through triplet network and contrastive learning
by: Deyu Meng, et al.
Published: (2025-09-01) -
Paraphrase and translation: the importance of being close [version 1; peer review: 2 approved]
by: Diana Santos, et al.
Published: (2025-02-01) -
Paraphrase detection for Urdu language text using fine-tune BiLSTM framework
by: Muhammad Ali Aslam, et al.
Published: (2025-05-01) -
Study of the Application of Text Augmentation with Paraphrasing to Overcome Imbalanced Data in Indonesian Text Classification
by: Mutiara Indryan Sari, et al.
Published: (2025-04-01)