Nancy F. Chen

Metacognitive regulation emerges as a key differentiator for deeper collaboration in human-AI interactions, reshaping our understanding of dialogue dynamics.

Forgetting specific data without sacrificing model performance just got a theoretical boost—HAMU guarantees improvements in unlearning quality based on data hardness.

Fine-tuning LLMs to emulate specific cultural values improves average accuracy, but simultaneously widens the disparity in performance between demographic subgroups.

LLMs can achieve state-of-the-art reasoning accuracy with significantly fewer tokens by rewarding intermediate reasoning steps that maximize information gain and maintain monotonic progress.