Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has
garnered growing interest for its potential to justify the prediction of black-box machine …

The task of empowering large language models (LLMs) to accurately express their
confidence, referred to as confidence elicitation, is essential in ensuring reliable and …

Large Vision-Language Models (LVLMs) have recently achieved remarkable success.
However, LVLMs are still plagued by the hallucination problem, which limits the practicality …

In recent years, there have been remarkable advancements in node classification achieved
by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels …

Recent research has made significant strides in applying alignment techniques to enhance
the helpfulness and harmlessness of large language models (LLMs) in accordance with …

Widely deployed large language models (LLMs) can produce convincing yet incorrect
outputs, potentially misleading users who may rely on them as if they were correct. To …

Widely used language models (LMs) are typically built by scaling up a two-stage training
pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-tuning …

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model
into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the …

We introduce BSDetector, a method for detecting bad and speculative answers from a
pretrained Large Language Model by estimating a numeric confidence score for any output …