Hongliang Dai

Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model’s true epistemic state. While recent mechanistic approaches leverage hidden state dynamics, they typically aggreg...

Fine-grained entity typing (FET) aims to assign semantically rich and contextually appropriate types to entity mentions. While recent studies have explored the use of large language models (LLMs) for this task, two key challenges persist. First, FET typically involves a large number of entity typ...

Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency. While prior works attempt to compress CoT via external compressor, they often fail to align with the model’s internal reasoning dynamics, resulting...