Update README.md

README.md

@@ -80,14 +80,26 @@ We introduce a large semi-automatically generated dataset of ~400,000 descriptiv
 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,
+@inproceedings{garcia-ferrero-etal-2023-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)",
+ 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.",
 }
 ```