The driving factors behind the development of large language models (LLMs) with
impressive learning capabilities are their colossal model sizes and extensive training …

The crystallization of modeling methods around the Transformer architecture has been a
boon for practitioners. Simple, well-motivated architectural variations can transfer across …

The BLOOM model is a large publicly available multilingual language model, but its
pretraining was limited to 46 languages. To extend the benefits of BLOOM to other …

Common Crawl is the largest freely available collection of web crawl data and one of the
most important sources of pre-training data for large language models (LLMs). It is used so …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

Abstract Large Language Models (LLMs) continue to grow, reaching hundreds of billions of
parameters and making it challenging for Deep Learning practitioners with resource …

This work considers the problem of Open-world Entity Profiling, a sub-domain of Open-world
Information Extraction (Open-world IE). Unlike the conventional closed-world IE, Open-world …

We consider the problem of Open-world Information Extraction (Open-world IE), which
extracts comprehensive entity profiles from unstructured texts. Different from the …

We present Spacerini, a modular framework for seamless building and deployment of
interactive search applications, designed to facilitate the qualitative analysis of large scale …

Pre-training Large Language Models (LLMs) require massive amounts of text data, and the
performance of the LLMs typically correlates with the scale and quality of the datasets. This …