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Clickbait Challenge 2017
The Clickbait Challenge 2017 dataset, a collection of social media posts and their corresponding article titles, used for clickbait detection. -
Diggs dataset
The dataset used for testing the sLDA model [16]. -
Fake News Challenge Stage 1 (FNC-1)
The FNC-1 dataset is a supervised classification task for stance detection, where the goal is to automatically predict the labels in a supervised classification task. -
ImageNet and SST2 datasets
The dataset used in this study for image and text classification tasks. -
LLM dataset
The dataset used in this paper is not explicitly described, but it is mentioned that it is a large language model (LLM) and that the authors used it to train and evaluate their... -
MMLU dataset
The dataset used in the paper is the Multitask Language Understanding (MMLU) dataset, which consists of 57 tasks from Science, Technology, Engineering, and Math (STEM),... -
SST-2, Irony, IronyB, TREC6, and SNIPS
The dataset used in this paper is SST-2, Irony, IronyB, TREC6, and SNIPS. -
CIFAR-100 and AGNews
Two datasets used for multi-task learning, CIFAR-100 and AGNews. -
Socher et al. (2013) dataset
The dataset used in the paper is a large-scale corpus of movie reviews from the Socher et al. (2013) dataset. -
Cross-topic Argument Mining from Heterogeneous Sources
Cross-topic Argument Mining from Heterogeneous Sources. -
Semeval-2016 Task 6: Detecting stance in tweets
Semeval-2016 Task 6: Detecting stance in tweets. -
Few-Shot Stance Detection via Target-Aware Prompt Distillation
Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot... -
A Million News Headlines, Fake and real news, Getting Real about Fake News
The dataset is a combination of 3 singular datasets: A Million News Headlines, Fake and real news, Getting Real about Fake News. -
Rotten Tomatoes
The Rotten Tomatoes dataset has 5331 positive and 5331 negative review sentences. -
HONEST Race
The dataset used for toxicity and stereotype mitigation task, which consists of 25 thousand examples of positive and negative movie reviews. -
Harry Potter unlearning dataset
The dataset used in the paper is a concatenation of the original Harry Potter books and synthetic discussions, blog posts, and wiki-like entries about the books. -
Equity Evaluation Corpus (EEC)
The dataset used in the paper is the Equity Evaluation Corpus (EEC) for emotion prediction, which contains a balanced dataset of sentences with emotions.
