UCLA NLP Seminar Series

Speaker Bio:Shrimai Prabhumoye is a Senior Research Scientist at NVIDIA’s Applied Deep Learning Research Group and an Adjunct Professor at Boston University. Her work advances the state of large language models (LLMs) through innovations in pretraining algorithms that emphasize data selection, blending, and ordering strategies, as well as the use of reinforcement-based objectives to instill foundational reasoning abilities. She is the lead contributor to NVIDIA’s Nemotron family of models, driving progress in data and reasoning. Shrimai earned her PhD in Computer Science from Carnegie Mellon University, where she focused on controllable text generation and computational ethics. Her research has been featured in VentureBeat, Forbes, and TechCrunch.

Abstract:This talk presents recent progress in pretraining algorithm design for large language models (LLMs), emphasizing the role of data ordering, reasoning-centric data integration, and reinforcement-based objectives in shaping model capability. We introduce a two-phase pretraining framework that formalizes strategies for data selection, blending, and sequencing, and demonstrate that front-loading reasoning-rich data during pretraining yields persistent gains in reasoning accuracy that post-training alone cannot reproduce. Further, we propose Reinforcement Learning during Pretraining (RLP) — a reinforcement-based objective that treats chain-of-thought generation as exploratory behavior, rewarding trajectories that maximize token-level information gain. Empirical results across diverse model scales show that structured data ordering combined with RLP improves downstream accuracies, and reasoning generalization, establishing a principled approach for integrating reasoning as a first-class objective in LLM pretraining.

Speaker Bio:Swabha Swayamdipta is an Assistant Professor of Computer Science and a co-Associate Director of the Center for AI and Society at the University of Southern California. Her research interests are in natural language processing and machine learning, with a primary interest in the evaluation of generative models of language, understanding the behavior of language models, and designing language technologies for societal good. At USC, Swabha leads the Data, Interpretability, Language and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, followed by a postdoc at the Allen Institute for AI and the University of Washington. Her work has received outstanding paper awards at EMNLP 2024, ICML 2022, NeurIPS 2021 and ACL 2020. Her research has been supported by awards from NIH, NSF, Apple, the Allen Institute for AI, Intel Labs, the Zumberge Foundation, the Simons Foundation, and a WiSE Gabilan Fellowship.

Abstract:The majority of popular language models today are both large-scale and close-sourced, making studying their behavior quite challenging. This talk tries to answer how much we can learn from limited observations of language model behavior. First, we show that language models can be reliably evaluated using even randomly selected microbenchmarks of a certain size. Second, we use language model outputs, i.e. next-token probability distributions, to build prompt inversion attacks to reveal hidden prompts with high accuracy. These findings highlight the importance of scientific research into large language models without access to large computation resources, while still allowing accountability for the providers, as well as efficient and reliable evaluation.

Speaker Bio:Christopher Potts is Professor of Linguistics and, by courtesy, of Computer Science at Stanford, and a faculty member in the Stanford NLP Group and the Stanford AI Lab. His research group uses computational methods to explore topics in context-dependent language use, systematicity and compositionality, model interpretability, information retrieval, and foundation model programming. This research combines methods from linguistics, cognitive psychology, and computer science, in the service of both scientific discovery and technology development.

Abstract:Kuditipudi, Huang, Zhu et al. (2025, "Blackbox model provenance via palimpsestic membership inference") provide an extremely effective, lightweight family of tests for whether one language model M1 is a derivative of another language model M2. The basic form of the test works like this: measure the correlation between the training data ordering for M1 and the likelihood that M2 assigns to those training examples. Fascinating … but how on earth did the authors come up with this? The tests were primarily developed by the three lead authors (Rohith, Jing, and Sally; henceforth RJS), and we may never know precisely what mix of genius, cosmic inspiration, and deep scholarship was at work here. However, I think we can see, at least in retrospect, that the necessary ingredients for proposing this test were basically present in the existing literature already. This talk will review that evidence, taking us on a hypothetical journey to these model provenance tests. I'll close by reviewing what we know the tests can teach us, and speculating about potential new applications and extensions of these ideas.

Speaker Bio:Liwei Jiang is a final-year PhD candidate in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where her research centers on humanistic, pluralistic, and coevolutionary AI safety and alignment. She develops computational frameworks for modeling human morality, advances culturally grounded and pluralistic value alignment, designs holistic safeguards for large language models, and explores future-oriented coevolutionary paradigms that integrate human-AI and AI-AI learning dynamics. Her work appears in top venues across AI, ML, and NLP, earning multiple Oral, Spotlight, Outstanding Paper, and Best Paper distinctions. She received her BA in Computer Science and Mathematics from Colby College, graduating summa cum laude.

Abstract:"In this talk, I will explore a central challenge for the next generation of AI systems: how to ensure that AI aligns with human morality, values, and needs in a world where those values are complex, diverse, and constantly evolving. As AI becomes deeply embedded in social, creative, and safety-critical settings, alignment must move beyond high-level principles toward concrete scientific frameworks and deployable technical methods. My work develops such a foundation and advances alignment across three complementary directions. I will begin by discussing how we can model human morality and pluralistic values in ways that are computationally grounded yet deeply informed by philosophy and cognitive science. This includes building representations of commonsense morality, cultural variation, and individual preferences, and showing how these learned structures can guide decision-making in large language models. Next, I will describe a line of research focused on putting value alignment into practice by developing holistic safety frameworks for language models. These include integrated red-teaming and defense pipelines, multilingual moderation tools, and system-level mechanisms that make LLMs steerable, controllable, and robust to evolving risks in real-world environments. Finally, I will turn to a forward-looking perspective on alignment: the idea of human–AI coevolution. I will outline how AI can augment human capabilities, how AI-to-AI interaction can drive iterative self-improvement, and how humans and AI together can form synergistic feedback loops that enable more capable, adaptive, and beneficial systems. Across these threads, the talk presents a unified vision of humanistic, pluralistic, and coevolutionary AI alignment—one that integrates moral reasoning, technical safety, and human–AI collaboration to support a future where AI systems act in ways that are meaningfully aligned with human values and ultimately contribute to human flourishing."