Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

Large datasets have become commonplace in NLP research. However, the increased
emphasis on data quantity has made it challenging to assess the quality of data. We …

Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple
calculations is an important skill of AI systems. While many datasets and models have been …

In recent years, progress in NLU has been driven by benchmarks. These benchmarks are
typically collected by crowdsourcing, where annotators write examples based on annotation …

Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

Recently, dataset-generation-based zero-shot learning has shown promising results by
training a task-specific model with a dataset synthesized from large pre-trained language …

Data modification, either via additional training datasets, data augmentation, debiasing, and
dataset filtering, has been proposed as an effective solution for generalizing to out-of …

Topic models are a class of unsupervised learning algorithms for detecting the semantic
structure within a text corpus. Together with a subsequent dimensionality reduction …

Knowledge of questions' difficulty level helps a teacher in several ways, such as estimating
students' potential quickly by asking carefully selected questions and improving quality of …

Deep Learning research has been largely accelerated by the development of huge datasets
such as Imagenet. The general trend has been to create big datasets to make a deep neural …