Verifiers or reward models are often used to enhance the reasoning performance of large
language models (LLMs). A common approach is the Best-of-N method, where N candidate …

Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations.
To address these, studies prefixed with" Self-" such as Self-Consistency, Self-Improve, and …

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

Estimating the uncertainty of responses of Large Language Models~(LLMs) remains a
critical challenge. While recent Bayesian methods have demonstrated effectiveness in …

Uncertainty quantification (UQ) is crucial for ensuring the safe deployment of large language
model, particularly in high-stakes applications where hallucinations can be harmful …

Tokenization is a necessary component within the current architecture of many language
models, including the transformer-based large language models (LLMs) of Generative AI …

Large Language Models (LLMs) have shown remarkable potential in reasoning while they
still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of …

Despite possessing impressive skills, Large Language Models (LLMs) often fail
unpredictably, demonstrating inconsistent success in even basic common sense reasoning …

Large language models (LLMs) excel at retrieving information from lengthy text, but their
vision-language counterparts (VLMs) face difficulties with hour-long videos, especially for …

Knowing when a language model is uncertain about its generations is a key challenge for
enhancing LLMs' safety and reliability. An increasing issue in the field of Uncertainty …