metadata

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<n<1M

"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

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 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:

@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",
}