3 papers
cs.LG2026
Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning
Qihao Wen, Jiahao Wang, Yang Nan +3
Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleading outputs. For responsible LLM a…
cs.LG2026
Interpretable Probability Estimation with LLMs via Shapley Reconstruction
Yang Nan, Qihao Wen, Jiahao Wang +4
Large Language Models (LLMs) demonstrate potential to estimate the probability of uncertain events, by leveraging their extensive knowledge and reasoning capabilities. This ability…
cs.CL2025
Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty?
Yang Nan, Pengfei He, Ravi Tandon +1
Large language models (LLMs) have delivered significant breakthroughs across diverse domains but can still produce unreliable or misleading outputs, posing critical challenges for…