Showing stat.MLShow all
2 papers · 1 filter
stat.ML2026
Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration
Erica Zhang, Naomi Sagan, Danny Tse +3
Large language models (LLMs) encode rich semantic knowledge that can be useful for supervised learning, but their outputs are unreliable as statistical priors: they may be noisy, m…
stat.ML2026
When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions
Lezhi Tan, Naomi Sagan, Lihua Lei +1
AI systems increasingly assist human decision making by producing preliminary assessments of complex inputs. However, such AI-generated assessments can often be noisy or systematic…