In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture,
healthcare, entertainment, and other industries. Most of these systems are developed with …

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 proliferation of open-source Large Language Models (LLMs) from various institutions
has highlighted the urgent need for comprehensive evaluation methods. However, current …

Background Artificial intelligence (AI) applications in health care have been effective in
many areas of medicine, but they are often trained for a single task using labelled data …

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to
ensure that they align with human values. For instance, in radiology report generation …

This study aims to address the pervasive challenge of quantifying uncertainty in large
language models (LLMs) without logit-access. Conformal Prediction (CP), known for its …

We develop new conformal inference methods for obtaining validity guarantees on the
output of large language models (LLMs). Prior work in conformal language modeling …

The rapid proliferation of large language models and natural language processing (NLP)
applications creates a crucial need for uncertainty quantification to mitigate risks such as …