UCLA NLP Seminar Series

Speaker Bio: Sean Welleck is an Assistant Professor at Carnegie Mellon University, where he leads the Machine Learning, Language, and Logic (L3) Lab. His areas of focus include generative models, algorithms for large language models, and AI for code, science, and mathematics. Sean received a Ph.D. from New York University. He was a postdoctoral scholar at the University of Washington and the Allen Institute for Artificial Intelligence. He is a recipient of a NeurIPS 2021 Outstanding Paper Award, and two NVIDIA AI Pioneering Research Awards.

Abstract: One of the most striking findings in modern research on large language models (LLMs) is that scaling up compute at training time leads to better final results. However, there is another lesser-mentioned scaling phenomenon, where adopting more sophisticated methods and/or scaling compute at inference time can result in significantly better outputs from LLMs. In this talk, I will talk about our lab's recent work on using inference-time strategies to enable better reasoning. This includes training models to think prior to steps of formal mathematical proving, leveraging strong evaluation models to enable easy-to-hard generalization, and inference scaling laws that optimally balance cost and performance. Together, these advances point to a new paradigm of scaling compute at inference time.

Speaker Bio: Sara Hooker leads Cohere For AI, the dedicated research arm of Cohere. Cohere For AI seeks to solve complex machine learning problems and supports fundamental research that explores the unknown. With a long track-record of impactful research at Google Brain, Sara brings a wealth of knowledge from across machine learning. Her work has focused on model efficiency training techniques and optimizing for models that fulfill multiple desired criteria -- interpretable, efficient, fair and robust. Sara leads a team of researchers and engineers working on making large language models more efficient, safe and grounded. Sara is currently on Kaggle's ML Advisory Research Board and serves on the World Economic Forum council on the Future of Artificial Intelligence.

Speaker Bio: Natasha Jaques is an Assistant Professor of Computer Science and Engineering at the University of Washington, and a Senior Research Scientist at Google DeepMind. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. During her PhD at MIT, she developed techniques for learning from human feedback signals to train language models which were later built on by OpenAI’s series of work on Reinforcement Learning from Human Feedback (RLHF). In the multi-agent space, she has developed techniques for improving coordination through social influence, and unsupervised environment design. Natasha’s work has received various awards, including Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, and the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine, and on CBC radio, among others. Natasha earned her Masters degree from the University of British Columbia, undergraduate degrees in Computer Science and Psychology from the University of Regina, and was a postdoctoral fellow at UC Berkeley.

Abstract: If AGI is right around the corner, why are AI agents still so bad at so many tasks? AI still fails to coordinate effectively with other agents, follow natural language instructions to complete embodied tasks, and generalize to circumstances not encountered during training. Even in pure language settings like dialog, AI still fails to adapt to the needs of individual users, instead aligning to a single set of values that may ignore the needs of minority groups. In this talk, I will argue that Social Learning is a key facet of intelligence that enables both humans and animals to easily adapt to new circumstances, coordinate with different people, and acquire complex behaviors. By improving the social intelligence of AI agents, we can get a step closer to adaptive, flexible, generalist agents which better align to diverse human values. This talk will overview recent work in the Social Reinforcement Learning lab, describing how to enable pluralistic alignment of large language models using human feedback, smooth coordination with diverse human partners, and improve social reasoning for understanding natural language commands.