{
 "id": "tum-nlp/cannot-dataset",
 "owner": "tum-nlp",
 "name": "cannot-dataset",
 "downloads": 100,
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 "last_modified": "2025-10-23T14:09:16.000Z",
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 "citation": "@inproceedings{anschutz-etal-2023-correct,\n    title = \"This is not correct! Negation-aware Evaluation of Language Generation Systems\",\n    author = {Ansch{\\\"u}tz, Miriam  and\n      Miguel Lozano, Diego  and\n      Groh, Georg},\n    editor = \"Keet, C. Maria  and\n      Lee, Hung-Yi  and\n      Zarrie{\\ss}, Sina\",\n    booktitle = \"Proceedings of the 16th International Natural Language Generation Conference\",\n    month = sep,\n    year = \"2023\",\n    address = \"Prague, Czechia\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2023.inlg-main.12/\",\n    doi = \"10.18653/v1/2023.inlg-main.12\",\n    pages = \"163--175\",\n    abstract = \"Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and used it to create the CANNOT negation evaluation dataset. Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity. Evaluating these models on existing benchmarks shows that our fine-tuned models outperform existing metrics on the negated sentences by far while preserving their base models' performances on other perturbations.\"\n}",
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 "revision": "b56975812b0bc997ef91f91969de84fe082c0650",
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