The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Parafusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity
Open accessAbstract
Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. Moreover, these datasets often contain hate speech and noise, and may unintentionally include non-English language sentences. This research introduces ParaFusion, a large-scale, high-quality English paraphrase dataset developed using Large Language Models (LLM) to address these challenges. ParaFusion augments existing datasets with high-quality data, significantly enhancing both lexical and syntactic diversity while maintaining close semantic similarity. It also mitigates the presence of hate speech and reduces noise, ensuring a cleaner and more focused English dataset. Results show that ParaFusion offers at least a 25% improvement in both syntactic and lexical diversity, measured across several metrics for each data source. The paper also aims to set a gold standard for paraphrase evaluation as it contains one of the most comprehensive evaluation strategies to date. The results underscore the potential of ParaFusion as a valuable resource for improving NLP applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Currently, there are 2 major streams of LLM-generated text detectors: (i) zero-shot classifiers [25, 33], which identify LLM-generated text based on the pattern and characteristics of the input, and (ii) watermark detectors [17], which rely on detecting the presence of watermarks which are imprinted into the text during the generation process [16], and are effective in the cases where the waterm”
From Understanding the effects of human-written paraphrases in LLM-generated text detection (2025) · cites this paper as [17]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“[17] performed the attack by replacing words with tokens generated from T5 model and noticed a significant watermark degradation with AUROC decreasing from 99.8% to 69.6%.”
From Understanding the effects of human-written paraphrases in LLM-generated text detection (2025) · cites this paper as [17]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The watermark detector classifies text by computing the number of green tokens and the probability of its existence in the given input [17].”
From Understanding the effects of human-written paraphrases in LLM-generated text detection (2025) · cites this paper as [17]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Call for Clarity in Reporting BLEU Scores
Post, M. (2018) · Proceedings of the Third Conference on Machine Translation: Research Papers · doi:10.18653/v1/w18-6319 · Cited by 2,112
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., & Zhu, C. (2023) · arXiv · doi:10.48550/arxiv.2303.16634 · Cited by 948
Best practices for the human evaluation of automatically generated text
van der Lee, C., Gatt, A., van Miltenburg, E., Wubben, S., & Krahmer, E. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8643 · Cited by 205
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.07082 · cited 1×
“The ”Ted-F Score” and ”Ted-3 Score” are calculated by building constituency parse trees for the source and paraphrase sentences using Stanza [22], converting the trees to bracket notation using the NLTK library [23] and regex, and then using the APTED library [24] to calculate the Full Tree Edit…”
Method
Research works printed but not linked: 21
- K. R. McKeown, "Paraphrasing using given and new information in a question-answer system," in Proceedings of the 17th annual meeting on Association for Computational Linguistics -. La Jolla, California: Association for Computational Linguistics, 1979, p. 67. [Online]. Available: http://portal.acm.org/citation.cfm?doid=982163.982182
- M. Meteer and V. Shaked, "Strategies for effective paraphrasing," in Proceedings of the 12th conference on Computational linguistics -, vol. 2. Budapest, Hungry: Association for Computational Linguistics, 1988, pp. 431-436. [Online]. Available: http://portal.acm.org/citation.cfm?doid=991719.991724
- R. Kozlowski, K. F. McCoy, and K. Vijay-Shanker, "Generation of single-sentence paraphrases from predicate/argument structure using lexico-grammatical resources," in Proceedings of the second international workshop on Paraphrasing -, vol. 16. Sapporo, Japan: Association for Computational Linguistics, 2003, pp. 1-8. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1118984.1118985
- I. A. Bolshakov and A. Gelbukh, "Synonymous Paraphrasing Using WordNet and Internet," in Natural Language Processing and Information Systems, D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Y. Vardi, G. Weikum, F. Meziane, and E. Métais, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, vol. 3136, pp. 312-323, series Title: Lecture Notes in Computer Science. [Online]. Available: http://link.springer.com/10.1007/978-3-540-27779-8 27 · DOI
- J. Zhou and S. Bhat, "Paraphrase Generation: A Survey of the State of the Art," in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, M.-F. Moens, X. Huang, L. Specia, and S. W.-t. Yih, Eds. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 5075-5086. [Online]. Available: https://aclanthology.org/2021.emnlp-main.414
- OpenAI, "GPT-4 Technical Report," 2023, publisher: arXiv Version Number: 3. [Online]. Available: https://arxiv.org/abs/2303.08774 · arXiv
- J. Ganitkevitch, B. Van Durme, and C. Callison-Burch, "PPDB: The Paraphrase Database," in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Atlanta, Georgia: Association for Computational Linguistics, Jun. 2013, pp. 758-764. [Online]. Available: https://aclanthology.org/N13-1092
- W. Lan, S. Qiu, H. He, and W. Xu, "A Continuously Growing Dataset of Sentential Paraphrases," in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics, 2017, pp. 1224-1234. [Online]. Available: http://aclweb.org/anthology/D17-1126
- A. Fader, L. Zettlemoyer, and O. Etzioni, "Paraphrase-Driven Learning for Open Question Answering," in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Sofia, Bulgaria: Association for Computational Linguistics, Aug. 2013, pp. 1608-1618. [Online]. Available: https://aclanthology.org/P13-1158
- T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft COCO: Common Objects in Context," in Computer Vision -ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, vol. 8693, pp. 740-755, series Title: Lecture Notes in Computer Science. [Online]. Available: http://link.springer.com/10.1007/978-3-319-10602-1 48 · DOI
- W. B. Dolan and C. Brockett, "Automatically Constructing a Corpus of Sentential Paraphrases," in Proceedings of the Third International Workshop on Paraphrasing (IWP2005), 2005. [Online]. Available: https://aclanthology.org/I05-5002
- S. Iyer, N. Dandeka, and K. Csernai, "First Quora Dataset Release: Question Pairs," 2017. [On- line]. Available: https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs 13. J. Wieting and K. Gimpel, "ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics, 2018, pp. 451-462. [Online]. Available: http://aclweb.org/anthology/P18-1042
- J. E. Hu, R. Rudinger, M. Post, and B. Van Durme, "ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation," Jan. 2019, arXiv:1901.03644 [cs]. [Online]. Available: http://arxiv.org/abs/ 1901.03644 · arXiv
- J. E. Hu, A. Singh, N. Holzenberger, M. Post, and B. Van Durme, "Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering," in Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Hong Kong, China: Association for Computational Linguistics, 2019, pp. 44-54. [Online]. Available: https://www.aclweb.org/anthology/K19-1005
- Y. Zhang, J. Baldridge, and L. He, "PAWS: Paraphrase Adversaries from Word Scrambling," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 1298-1308. [Online]. Available: https://aclanthology.org/N19-1131
- T. Gao, X. Yao, and D. Chen, "SimCSE: Simple Contrastive Learning of Sentence Embeddings," in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 6894-6910. [Online]. Available: https: //aclanthology.org/2021.emnlp-main.552
- Y. Jiang, L. Zhang, and W. Wang, "Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning," in Findings of the Association for Computational Linguistics: EMNLP 2022. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 3021-3035. [Online]. Available: https://aclanthology.org/2022.findings-emnlp.220
- N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Nov. 2019. [Online]. Available: http://arxiv.org/abs/1908.10084 · arXiv
- S. Bird, E. Loper, and E. Klein, Natural Language Processing with Python. O'Reilly Media Inc., 2009.
- M. Pawlik and N. Augsten, "Efficient Computation of the Tree Edit Distance," ACM Transactions on Database Systems, vol. 40, no. 1, pp. 1-40, Mar. 2015. [Online]. Available: https://dl.acm.org/doi/10.1145/2699485 · DOI
- F. M. Zanzotto, A. Santilli, L. Ranaldi, D. Onorati, P. Tommasino, and F. Fallucchi, "KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations," in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online: Association for Computational Linguistics, Nov. 2020, pp. 256-267. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-main. 18
Cited by
Sorted by citations
Understanding the effects of human-written paraphrases in LLM-generated text detection
Lau, H. T., & Zubiaga, A. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100151 · Cited by 13
Metadata
- Authors
- Jayawardena, L., & Yapa, P.
- Affiliations
- Informatics Institute of Technology (LK)
- Venue
- Artificial Intelligence and Big Data
- Published
- 2024-02-24
- Pages
- 219-238
- OpenAlex
- W4392345938
- Cited by
- 4
- References
- 4
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Conference on Artificial Intelligence and Big Data (AIBD) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- paraphrase generation, paraphrase dataset, lexical diversity, syntactic diversity, large language models, data augmentation, noise reduction, paraphrase evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
