Finetuning language models on a collection of datasets phrased as instructions has been
shown to improve model performance and generalization to unseen tasks. In this paper we …

Multitask prompted finetuning (MTF) has been shown to help large language models
generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused …

The surprising ability of Large Language Models (LLMs) to perform well on complex
reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very …

Evaluation of Large Language Models (LLMs) is challenging because aligning to human
values requires the composition of multiple skills and the required set of skills varies …

The rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

Large language models (LLMs) have made significant strides in various natural language
processing (NLP) tasks. Recent research shows that the moderately-sized LLMs often …

The recent explosion of performance of large language models (LLMs) has changed the
field of Natural Language Processing (NLP) more abruptly and seismically than any other …

Numbers are crucial for various real-world domains such as finance, economics, and
science. Thus, understanding and reasoning with numbers are essential skills for language …

Long-term time series forecasting (LTSF) aims to predict future values of a time series given
the past values. The current state-of-the-art (SOTA) on this problem is attained in some …

Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for
scaling up large language models (LLMs). However, the reliability assessment of MoE lags …