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Eric Fosler-Lussier: Speech Recognition - 2000

Lecture given as part of CS 601.471/671 NLP: Self-supervised Models:
https://self-supervised.cs.jhu.edu/sp2024/

Speaker: Tianjian Li:
https://tianjianl.github.io/

Tutorial: Efficient language models -- Tianjian Li (JHU)

Abstract: High-quality datasets are crucial for improving the capabilities and training efficiency of large language models. However, current datasets are typically prepared in an ad hoc, heuristic way. In this talk, Sang Michael Xie will present principled approaches to improving and understanding language models centered on the pre-training data distribution. First, he will describe how to improve the efficiency of training multipurpose language models by optimizing the mixture of data sources with robust optimization. Second, he will discuss an efficient importance resampling method for selecting relevant data from trillion-token-scale web datasets for training a specialized model. Finally, he will introduce a first theoretical analysis of in-context learning, a key capability of language models to learn from examples in a textual prompt, that traces the capability back to modeling coherence structure in the pre-training data.

Speaker Biography: Sang Michael Xie is a computer science PhD student at Stanford University advised by Percy Liang and Tengyu Ma. His research focuses on data-centric machine learning for language models, understanding pre-training and adaptation, and pre-training and self-training methods for robust machine learning. Xie was awarded a NDSEG Fellowship and was previously a student researcher at Google Brain. His work has been recognized as one of Scientific American‘s World-Changing Ideas, published in flagship venues such as Science, and covered by media outlets including The New York Times, The Washington Post, Reuters, BBC News, IEEE Spectrum, and The Verge.

Data-distributional Approaches for Generalizable Language Models -- Sang Michael Xie (Stanford)

UBM based Acoustic Modeling for ASR (Daniel Povey) - 2009

Abstract:
I discuss the application of Foundation Models in Astronomy through the collaborative efforts of the UniverseTBD consortium with a mission to democratize Science for everyone. One of our key objectives is to overcome the limitations of general-purpose Foundation Models, such as producing limited information in specialized fields. To this end, we have developed the first specialized large language model for Astronomy, AstroLLaMa-1. This model, enhanced by exposure to domain-specific literature from the NASA Astrophysics Data System and ArXiv, demonstrates improved text completion and embedding capabilities over existent GPT models. I further discuss the potential of LLMs in generating complex scientific hypotheses and extracting meaningful insights from astronomy literature. Our findings, validated by human experts, demonstrate the LLM capability in informed scientific critique and uncover intriguing patterns in the embedding space, highlighting the potential of LLMs to augment scientific inquiry. I will also discuss preliminary work with the multi-modal model AstroLLaVA, which allows us to interact with astronomical images via natural language. Through the work of UniverseTBD, we aim to explore how artificial intelligence can assist human intelligence in Astronomy and, more broadly, Science.

Bio:
Ioana Ciucă, who goes by Jo, is an interdisciplinary Jubilee Joint Fellow at the Australian National University, working across the School of Computing and the Research School of Astronomy & Astrophysics. Before joining ANU, Jo finished her PhD in Astrophysics at University College London in the United Kingdom, where she worked at the intersection of Astronomy and Machine Learning to understand the formation and evolution history of our Galaxy, the Milky Way. Jo is now focusing on utilizing foundation models that benefit researchers everywhere, working alongside the UniverseTBD team of more than 30 astronomers, engineers, ML practitioners and enthusiasts worldwide.

A Universe To Be Decided: Towards Specialized Foundation Models for Advancing Astronomy

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