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DTSTART;TZID=America/New_York:20230208T120000
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CREATED:20230104T183022Z
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SUMMARY:ASSET Seminar: Using Large Language Models to Build Explainable Classifiers\, Chris Callison-Burch (University of Pennsylvania)
DESCRIPTION:ABSTRACT: \nI’ll present research on using large language models (LLMs) to build explainable classifiers.   I will show off work from my PhD students and collaborators on several recent research directions: \n\nImage classification with explainable features  (https://arxiv.org/abs/2211.11158)\nText classification with explainable features (work in progress)\nThe importance of faithfulness in explanations (https://arxiv.org/abs/2209.11326)\n(Time permitting) A faithful “chain of thought” LLM reasoner that produces code in its explanations (https://arxiv.org/abs/2301.13379)\n\nHere’s an example of the automatically generated concepts that we use for image classification in the first \nThe papers that I’ll present are joint work with:\nAdam Stein\, Ajay Patel\, Ansh Kothary\, Artemis Panagopoulou\, Daniel Jin\, Delip Rao\, Eric Wong\, Harry Li Zhang\, Kathleen McKeown\, Marianna Apidianaki\, Mark Yatskar\, Shenghao Zhou\, Shreya Havaldar\, Veronica Qing Lyu\, Yue Yang\, and othe
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tba-chris-callison-burch-university-of-pennsylvania/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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