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
multilinguality:
  - monolingual
source_datasets:
  - original
paperswithcode_id: this-is-not-a-dataset

"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

@inproceedings{garcia-ferrero-etal-2023-dataset,
    title = "This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models",
    author = "Garc{\'\i}a-Ferrero, Iker  and
      Altuna, Bego{\~n}a  and
      Alvez, Javier  and
      Gonzalez-Dios, Itziar  and
      Rigau, German",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.531",
    doi = "10.18653/v1/2023.emnlp-main.531",
    pages = "8596--8615",
    abstract = "Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to interpret negation, a crucial step in Natural Language Processing. We try to clarify the reasons for the sub-optimal performance of LLMs understanding negation. We introduce a large semi-automatically generated dataset of circa 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. We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the understanding of negation can be trained. Our findings show that, while LLMs are proficient at classifying affirmative sentences, they struggle with negative sentences and lack a deep understanding of negation, often relying on superficial cues. Although fine-tuning the models on negative sentences improves their performance, the lack of generalization in handling negation is persistent, highlighting the ongoing challenges of LLMs regarding negation understanding and generalization. The dataset and code are publicly available.",
}