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arXiv · 2409.12060

PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection Models

Andrianos Michail·Simon Clematide·Juri Opitz
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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

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Andrianos Michail, Simon Clematide, Juri Opitz. 2024-09-18. PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection Models. https://arxiv.org/abs/2409.12060

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