README.md

---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: paraphrase
    dtype: int64
  splits:
  - name: test
    num_bytes: 13558
    num_examples: 167
  download_size: 8253
  dataset_size: 13558
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- text-classification
language:
- en
pretty_name: True Paraphrases
size_categories:
- n<1K
---


# True Paraphrases Test Set

The True Paraphrases sentence/phrase pairs derived from the [AMR Annotation Guidelines](https://github.com/amrisi/amr-guidelines). It was introduced as part of the **PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models**. 

For more details, refer to the [original paper](https://arxiv.org/abs/2409.12060) that was presented at COLING 2025.

---

### Citation

If you use this dataset, please cite it using the following BibTeX entry:

```bibtex
@inproceedings{michail-etal-2025-paraphrasus,
    title = "{PARAPHRASUS}: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models",
    author = "Michail, Andrianos  and
      Clematide, Simon  and
      Opitz, Juri",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.585/",
    pages = "8749--8762",
    abstract = "The task of determining whether two texts are paraphrases has long been a challenge in NLP. However, the prevailing notion of paraphrase is often quite simplistic, offering only a limited view of the vast spectrum of paraphrase phenomena. Indeed, we find that evaluating models in a paraphrase dataset can leave uncertainty about their true semantic understanding. To alleviate this, we create PARAPHRASUS, a benchmark designed for multi-dimensional assessment, benchmarking and selection of paraphrase detection models. We find that paraphrase detection models under our fine-grained evaluation lens exhibit trade-offs that cannot be captured through a single classification dataset. Furthermore, PARAPHRASUS allows prompt calibration for different use cases, tailoring LLM models to specific strictness levels. PARAPHRASUS includes 3 challenges spanning over 10 datasets, including 8 repurposed and 2 newly annotated; we release it along with a benchmarking library at https://github.com/impresso/paraphrasus"
}