Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

We study the application of large language models to zero-shot and few-shot classification
of tabular data. We prompt the large language model with a serialization of the tabular data …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Foundation Models (FMs) are models trained on large corpora of data that, at very large
scale, can generalize to new tasks without any task-specific finetuning. As these models …

Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to
understand, process, and synthesize language. The authors of this paper believe that this …

Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such
tables, there has been tremendous progress on a variety of tasks in the area of table …

Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to
follow diverse human instructions and perform a wide range of tasks. However, when …

With the rapid proliferation of scientific literature, versatile academic knowledge services
increasingly rely on comprehensive academic graph mining. Despite the availability of …