4 papers
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…
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
Jiacheng Lin, Kun Qian, Arvind Srinivasan +15
Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete pi…
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…
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…