Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

Large language models (LLMs) exhibit a wide range of promising capabilities--from step-by-
step planning to commonsense reasoning--that may provide utility for robots, but remain …

The complexity and emergent autonomy of Generative AI systems introduce challenges in
predictability and legal compliance. This paper analyses some of the legal and regulatory …

There is much excitement about the opportunity to harness the power of large language
models (LLMs) when building problem-solving assistants. However, the standard …

Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of
large language models (LLMs). However, fine-tuned LLMs often become overconfident …

This paper investigates the capabilities of Large Language Models (LLMs) in the context of
understanding their knowledge and uncertainty over questions. Specifically, we focus on …

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a
predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the …

While Large Language Models (LLMs) have demonstrated remarkable potential in natural
language generation and instruction following, a persistent challenge lies in their …

Large Language Models (LLMs) have demonstrated remarkable capability in a variety of
NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty …