The remarkable performance of large language models (LLMs) in content generation,
coding, and common-sense reasoning has spurred widespread integration into many facets …

Large Language Models (LLMs) are increasingly employed in real-world applications,
driving the need to evaluate the trustworthiness of their generated text. To this end, reliable …

As large language models (LLMs) are increasingly deployed in user-facing applications,
building trust and maintaining safety by accurately quantifying a model's confidence in its …

Retrieval-augmented generation (RAG) improves large language models (LLMs) by using
external knowledge to guide response generation, reducing hallucinations. However, RAG …

In line with the principle of honesty, there has been a growing effort to train large language
models (LLMs) to generate outputs containing epistemic markers. However, evaluation in …

One important approach to improving the reliability of large language models (LLMs) is to
provide accurate confidence estimations regarding the correctness of their answers …

Large Language Models (LLMs) display formidable capabilities in generative tasks but also
pose potential risks due to their tendency to generate hallucinatory responses. Uncertainty …

Introduction The inability for Large Language Models (LLMs) to communicate uncertainty is
a significant barrier to their use in medicine. Before LLMs can be integrated into patient care …

Large Language Models (LLMs) have gained increasing attention for their impressive
capabilities, alongside concerns about the reliability arising from their potential to generate …