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Events for March 09, 2023

  • NL Seminar - • Enhancing Machine Translation with Large Language Models via Optimizing In Context Examples and Dictionary Based Prompting

    Thu, Mar 09, 2023 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Marjan Ghazvininejad, FAIR, Facebook AI Research

    Talk Title: Enhancing Machine Translation with Large Language Models via Optimizing In Context Examples and Dictionary Based Prompting

    Abstract: REMINDER:
    This Talk will be a Live Broadcast Only, It "Will Not" be recorded.

    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.

    Large language models LLMs have revolutionized natural language processing by demonstrating impressive abilities to perform a wide range of tasks, including machine translation MT. However, the quality and domain of the in-context examples used to prompt these models can significantly impact their performance for specific tasks. In this talk, I will discuss two recent papers that propose to optimize in-context examples and leverage bilingual dictionaries to enhance the quality and controllability of MT with LLMs. First, I will explore the impact of in-context examples on the translation quality of LLMs and highlight the challenges of selecting good examples in both in-domain and out-of-domain settings. Then, I will discuss how we can leverage bilingual dictionaries to provide fine-grained phrase-level control hints in the prompts of LLMs.

    Biography: Marjan Ghazvininejad is a senior research scientist at Facebook AI Research. She received her Ph.D. at the University of Southern California on neural creative language generation. Her research interests include text representation, language generation, and machine translation. Her recent research has focused on how to optimize the use of large language models in various applications.

    Host: Jon May and Justin Cho

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://usc.zoom.us/j/96863901584

    Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689

    WebCast Link: https://usc.zoom.us/j/96863901584

    Audiences: Everyone Is Invited

    Contact: Pete Zamar

    Event Link: https://nlg.isi.edu/nl-seminar/

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  • CS Colloquium: Alexander Rodríguez (Georgia Tech) - AI for Public Health: Epidemic Forecasting Through a Data-Centric Lens

    Thu, Mar 09, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Alexander Rodríguez , Georgia Tech

    Talk Title: AI for Public Health: Epidemic Forecasting Through a Data-Centric Lens

    Series: CS Colloquium

    Abstract: Epidemic forecasting is a crucial tool for public health decision making and planning. There is, however, a limited understanding of how epidemics spread, largely due to other complex dynamics, most notably social and pathogen dynamics. With the increasing availability of real-time multimodal data, a new opportunity has emerged for capturing previously unobservable facets of the spatiotemporal dynamics of epidemics. In this regard, my work brings a data-centric perspective to public health via methodological advances in AI at the intersection of time series analysis, spatiotemporal mining, scientific ML, and multi-agent systems. Toward realizing the potential of AI in public health, I addressed multiple challenges stemming from the domain such as data scarcity, distributional changes, and issues arising from real-time deployment to enable our support of CDC's COVID-19 response. This talk will cover methods to address these challenges with novel deep learning architectures for real-time response to disease outbreaks and new techniques for end-to-end learning with mechanistic epidemiological models-”based on differential equations and agent-based models-”that bridge ML advances and traditional domain knowledge to leverage individual merits. I will conclude by discussing challenges and opportunities in public health for data and computer scientists.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Alexander Rodríguez is a PhD candidate in the College of Computing at Georgia Tech, advised by Prof. B. Aditya Prakash. His research is at the intersection of machine learning, time series, and scientific modeling, and his main application domains are public health and community resilience. He has published at top venues such as AAAI, NeurIPS, ICLR, KDD, WWW, AAMAS, PNAS and has organized workshops and tutorials at AAAI and KDD. His work won the best paper award at ICML AI4ABM 2022 and was awarded the 1st place in the Facebook/CMU COVID-19 Challenge and the 2nd place in the C3.ai COVID-19 Grand Challenge. He was also invited to the Heidelberg Laureate Forum in 2022, and named a 'Rising Star in Data Science' by the University of Chicago Data Science Institute in 2021 and a 'Rising Star in ML & AI' by the University of Southern California in 2022.

    Host: Bistra Dilkina

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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