This paper provides a detailed examination of the advancements and applications of large
language models in the healthcare sector, with a particular emphasis on clinical …

Generative models are rapidly gaining popularity and being integrated into everyday
applications, raising concerns over their safe use as various vulnerabilities are exposed. In …

Abstract Large Language Models (LLMs) have demonstrated remarkable advancements
across diverse domains, manifesting considerable capabilities in evolutionary computation …

As language models grow ever larger, so do their vocabularies. This has shifted the memory
footprint of LLMs during training disproportionately to one single layer: the cross-entropy in …

Extending context window sizes allows large language models (LLMs) to process longer
sequences and handle more complex tasks. Rotary Positional Embedding (RoPE) has …

We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To
this end, we evaluate their performance on pairs of existing math word problems together so …

The success of artificial intelligence (AI) in materials research heavily relies on the integrity
of structured data and the construction of precise descriptors. In this study, we present an …

A growing number of machine learning scenarios rely on knowledge distillation where one
uses the output of a surrogate model as labels to supervise the training of a target model. In …

As the landscape of large language models expands, efficiently finetuning for specific tasks
becomes increasingly crucial. At the same time, the landscape of parameter-efficient …

Foundation models have revolutionized natural language processing and artificial
intelligence, significantly enhancing how machines comprehend and generate human …