8 citations · 8 across the 5 of their papers we have counts for
7 papers
Metacognition in LLMs: Foundations, Progress, and Opportunities
Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu +3
Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following
Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu +2
Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved…
Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback
Gabrielle Kaili-May Liu
Is it possible for machines to think like humans? And if it is, how should we go about teaching them to do so? As early as 1950, Alan Turing stated that we ought to teach machines…
Streaming Inference for Infinite Non-Stationary Clustering
Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du +2
Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here,…
Weight Friction: A Simple Method to Overcome Catastrophic Forgetting and Enable Continual Learning
Gabrielle K. Liu
In recent years, deep neural networks have found success in replicating human-level cognitive skills, yet they suffer from several major obstacles. One significant limitation is th…