196 datasets found

The dataset used in the paper is a counting probe dataset, which consists of images and corresponding questions or statements about the number of entities in the image.

The dataset is used for neural collaborative filtering, which is a type of collaborative filtering that uses neural networks to learn the relationships between users and items.

The IMDB-RLHF-Pair dataset is generated by IMDB, and responses with positive sentiment are preferred.

The Stack-Exchange-Paired dataset contains questions and answers from the Stack Overflow dataset, where answers with more votes are preferred.

The XQuAD dataset is a multilingual question answering dataset.

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there...

The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1].

The dataset used in this paper is a real-world dataset from Quora, containing 372,818 questions and 1,739,222 answers associated with topics, upvotes, timestamps, etc.

The Stanford Question Answering Dataset (SQuAD 2.0) supplements the SQuAD 1.1 with over 50K unanswerable questions.

The Stanford Question Answering Dataset (SQuAD 1.1) is a dataset of more than 100K questions which all can be answered by locating a span of text from the corresponding context...

The AVQA dataset contains 57,015 videos and 57,335 question-and-answer pairs.

The Music-AVQA dataset contains multiple question-and-answer pairs, with 9,288 videos and 45,867 question-and-answer pairs.

Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer.

The dataset Yahoo Answers contains 730,000 questions and answers.

The Bing dataset is a large-scale dataset for natural language understanding and question answering.

The MS MARCO dataset is a large-scale dataset for natural language understanding and question answering.

A collection of heterogeneous visual reasoning data sets and an interesting benchmark for two reasons: First, visual reasoning programs tend to be large (in current program...

The RJUA-QA dataset is a urological domain open-source dataset extracted from real-world medical records with 2132 QA pairs.

The CPQA dataset consists of a cloud product knowledge graph (CPKG) and QA pairs. The dataset is used for domain-specific question answering (QA) tasks.

ProCQA is a large-scale community-based programming question answering dataset mined from StackOverflow with strict filtering strategies for quality and fairness control.