Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Abstract Recent advances in Large Language Models (LLMs) have achieved remarkable
breakthroughs in understanding and responding to user intents. However, their performance …

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

The strength of modern generative models lies in their ability to be controlled through
prompts. Hard prompts comprise interpretable words and tokens, and are typically hand …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

Prompting has shown impressive success in enabling large pretrained language models
(LMs) to perform diverse NLP tasks, especially when only few downstream data are …

Transformer-based large language models (LLMs) provide a powerful foundation for natural
language tasks in large-scale customer-facing applications. However, studies that explore …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Large Language Models (LLMs) have shown impressive performance as general purpose
agents, but their abilities remain highly dependent on prompts which are hand written with …