--- license: apache-2.0 dataset_info: features: - name: pattern_id dtype: int64 - name: pattern dtype: string - name: test_id dtype: int64 - name: negation_type dtype: string - name: semantic_type dtype: string - name: syntactic_scope dtype: string - name: isDistractor dtype: bool - name: label dtype: bool - name: sentence dtype: string splits: - name: train num_bytes: 41264658 num_examples: 268505 - name: validation num_bytes: 3056321 num_examples: 22514 - name: test num_bytes: 12684749 num_examples: 90281 download_size: 6311034 dataset_size: 57005728 task_categories: - text-classification language: - en tags: - commonsense - negation - LLMs - LLM pretty_name: This is NOT a Dataset size_categories: - 100K

"A Large Negation Benchmark to Challenge Large Language Models"

We introduce a large semi-automatically generated dataset of ~400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms that we use to evaluate LLMs.

- 📖 Paper: [This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (EMNLP'23)]() - 💻 Baseline Code and the Official Scorer: [https://github.com/hitz-zentroa/This-is-not-a-Dataset](https://github.com/hitz-zentroa/This-is-not-a-Dataset) # Data explanation - **pattern_id** (int): The ID of the pattern,in range [1,11] - **pattern** (str): The name of the pattern - **test_id** (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id - **negation_type** (str): Affirmation, verbal, non-verbal - **semantic_type** (str): None (for affirmative sentences), analytic, synthetic - **syntactic_scope** (str): None (for affirmative sentences), clausal, subclausal - **isDistractor** (bool): We use distractors (randonly selectec synsets) to generate false kwoledge. - **sentence** (str): The sentence. This is the input of the model - **label** (bool): The label of the example, True if the statement is true, False otherwise. This is the target of the model If you want to run experiments with this dataset, please, use the [Official Scorer](https://github.com/hitz-zentroa/This-is-not-a-Dataset#scorer) to ensure reproducibility and fairness. # Citation The paper will be presented at EMNLP 2023, the citation will be available soon. For now, you can use the following bibtex: ```bibtex @inproceedings{this-is-not-a-dataset, title = "This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models", author = "Iker García-Ferrero, Begoña Altuna, Javier Alvez, Itziar Gonzalez-Dios, German Rigau", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)", year = "2023", publisher = "Association for Computational Linguistics", } ```