23 datasets found

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...

The SQuAD 1.1 and SQuAD 2 datasets are used to evaluate the performance of the EQuANt model.

The BIG-Bench Hard dataset is derived from the original BIG-Bench evaluation suite, focusing on tasks that pose challenges to existing language models.

The paper proposes a method to leverage QA datasets for training generative language models to be context generators for a given question and answer.

LDC2015E86 is a dataset of abstract meaning representation (AMR) annotations for English.

The SQuAD dataset is a benchmark for natural language understanding tasks, including question answering and text classification.

FarFetched is a modular framework that enables people to verify any kind of textual claim based on the incorporated evidence from textual news sources.

The InsuranceQA dataset is a question answering dataset containing questions and answers.

The dataset used in the paper is a corpus of text containing approximately 10,000 examples, each a sequence of sentences extracted from books.

The QQP dataset contains more than 400k question pairs.

A large-scale dataset containing over 180K StockQA instances, built based on Chinese online stock forums.

Semantic parsing maps a user-issued natural language (NL) utterance to a machine-executable meaning representation (MR), such as λ−calculus (Zettlemoyer and Collins, 2005), SQL...

Semanitic parsing on freebase from question-answer pairs

RadQA: A question answering dataset to improve comprehension of radiology reports

The dataset is used for instruction-tuning of LLMs in multiple languages using reinforcement learning from human feedback.

The WIKIQA dataset is used for evaluation of the proposed QMWF-LM approach.

The dataset used in the paper is a multiple-choice reading comprehension dataset, which includes a passage, question, and answer. The passage is a script, and the question is a...

The Natural Questions dataset consists of questions extracted from web queries, with each question accompanied by a corresponding Wikipedia article containing the answer.

The dataset used for the experiments across ten models– ranging from bag-of-words models to pre-trained transformers– and find that a model having higher AUC does not necessarily...

The dataset is used for training and evaluating the MS MARCO model, a question answering model.