Publications

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks across large sets of documents. This paper introduces MDCure, an effective and scalable procedure for generating high-quality multi-document instruction tuning data to improve MD capabilities of any base LLM.

Metacognition of emotion refers to the ability to evaluate success at identifying one’s own emotional feelings and adjusting them accordingly. This paper establishes the first experimentally-validated procedure to reliably assess and quantify metacognition of emotion.

This paper discusses the social implications of reinforcement learning with human feedback (RLHF), identifying key social and ethical issues and discussing social impacts for stakeholders. Seven impact areas are examined, including misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce.

An undergraduate-level curriculum that cultivates understanding of the robot rights debate as informed by Western and Eastern principles, stepping toward filling existing gaps in knowledge and discourse and uniting the global community. Constructs and works through a foundational framework to engage students in critical thinking, understanding, and discussion of decisions regarding robot rights.

We define the Dynamical Chinese Restaurant Process (Dynamical CRP), a novel stochastic process that provides a non-stationary prior over cluster assignments and yields an efficient streaming variational inference algorithm. Experiments show the Dynamical CRP can be applied on diverse synthetic and real data with Gaussian and non-Gaussian likelihoods.

R-IBP is a novel recursive form of the Indian Buffet Process that makes feature models applicable to streaming data. It enables creation of new features online and in a probabilistic, principled manner. As a prior for feature models, R-IBP yields efficient inference over an unbounded number of latent features, with quasilinear average time complexity and logarithmic average space complexity.

The first experimentally quantitative index of metacognition of emotion. Advancing understanding of awareness toward subjective feelings.

Weight friction draws from principles in neuroscience and physics to help mitigate catatstrophic forgetting in neural networks. It operates over multiple task domains and has convergence comparable to SGD.

GFCCs can be powerful speech features for use in speech emotion recognition. This preliminary study suggests they are more suitable versus MFCCs for emotion and intensity classification tasks with neural networks.