BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-20723@www.clsp.jhu.edu DTSTAMP:20240329T140500Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nText simplification aims t o help audiences read and understand a piece of text through lexical\, syn tactic\, and discourse modifications\, while remaining faithful to its cen tral idea and meaning. Thanks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focu ses on sentence simplification\, transforming original\, more complex sent ences into simplified versions. In this talk\, I present new frontiers tha t focus on discourse operations. First\, we consider the challenging task of simplifying highly technical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinic al topics. We then propose a new metric to quantify stylistic differentiat es between the two\, and models for paragraph-level simplification. Second \, we present the first data-driven study of inserting elaborations and ex planations during simplification\, and illustrate the richness and complex ities of this phenomenon.
\nBiography
\nAbstract
\nLarge language models (LLM s) have demonstrated incredible power\, but they also possess vulnerabilit ies that can lead to misuse and potential attacks. In this presentation\, we will address two fundamental questions regarding the responsible utiliz ation of LLMs: (1) How can we accurately identify AI-generated text? (2) W hat measures can safeguard the intellectual property of LLMs? We will intr oduce two recent watermarking techniques designed for text and models\, re spectively. Our discussion will encompass the theoretical underpinnings th at ensure the correctness of watermark detection\, along with robustness a gainst evasion attacks. Furthermore\, we will showcase empirical evidence validating their effectiveness. These findings establish a solid technical groundwork for policymakers\, legal professionals\, and generative AI pra ctitioners alike.
\nBiography
\nLei Li is an Assistant Professor in Language Technology Institute at Carnegie Mellon Un iversity. He received Ph.D. from Carnegie Mellon University School of Comp uter Science. He is a recipient of ACL 2021 Best Paper Award\, CCF Young E lite Award in 2019\, CCF distinguished speaker in 2017\, Wu Wen-tsün AI pr ize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a facul ty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI La b in 2016 and led its research in NLP\, ML\, Robotics\, and Drug Discovery . He launched ByteDance’s machine translation system VolcTrans and AI writ ing system Xiaomingbot\, serving one billion users.
DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24507@www.clsp.jhu.edu DTSTAMP:20240329T140500Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nHistory repeats itself\, s ometimes in a bad way. Preventing natural or man-made disasters requires b eing aware of these patterns and taking pre-emptive action to address and reduce them\, or ideally\, eliminate them. Emerging events\, such as the C OVID pandemic and the Ukraine Crisis\, require a time-sensitive comprehens ive understanding of the situation to allow for appropriate decision-makin g and effective action response. Automated generation of situation reports can significantly reduce the time\, effort\, and cost for domain experts when preparing their official human-curated reports. However\, AI research toward this goal has been very limited\, and no successful trials have ye t been conducted to automate such report generation and “what-if” disaster forecasting. Pre-existing natural language processing and information ret rieval techniques are insufficient to identify\, locate\, and summarize im portant information\, and lack detailed\, structured\, and strategic aware ness. In this talk I will present SmartBook\, a novel framework that canno t be solved by large language models alone\, to consume large volumes of m ultimodal multilingual news data and produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence through multimodal knowledge extraction\, claim detec tion\, fact checking\, misinformation detection and factual error correcti on. Furthermore\, SmartBook can also serve as a novel news event simulator \, or an intelligent prophetess. Given “What-if” conditions and dimension s elicited from a domain expert user concerning a disaster scenario\, Smar tBook will induce schemas from historical events\, and automatically gener ate a complex event graph along with a timeline of news articles that desc ribe new simulated events and character-centric stories based on a new Λ-s haped attention mask that can generate text with infinite length. By effec tively simulating disaster scenarios in both event graph and natural langu age format\, we expect SmartBook will greatly assist humanitarian workers and policymakers to exercise reality checks\, and thus better prevent and respond to future disasters.
\nBio
\nHeng Ji is a professor at Computer Science Department\, and an affiliated faculty member at Electrical and Computer Engineering Department and Coordinated S cience Laboratory of University of Illinois Urbana-Champaign. She is an Am azon Scholar. She is the Founding Director of Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE). She received her B.A. and M. A. in Computational Linguistics from Tsinghua University\, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing\, especially on Multimedia Multilingual Information Extraction\, Knowledge-enhanced Large Language Mo dels\, Knowledge-driven Generation and Conversational AI. She was selected as a Young Scientist to attend the 6th World Laureates Association Forum\ , and selected to participate in DARPA AI Forward in 2023. She was selecte d as “Young Scientist” and a member of the Global Future Council on the Fu ture of Computing by the World Economic Forum in 2016 and 2017. The awards she received include Women Leaders of Conversational AI (Class of 2023) b y Project Voice\, “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013\, NSF CAREER award in 2009\, PACLIC2012 Best paper runner-up\, “Best of ICDM2013” paper award\, “Best of SDM2013” paper award\, ACL2018 Best De mo paper nomination\, ACL2020 Best Demo Paper Award\, NAACL2021 Best Demo Paper Award\, Google Research Award in 2009 and 2014\, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invi ted to testify to the U.S. House Cybersecurity\, Data Analytics\, & IT Com mittee as an AI expert in 2023. She was invited by the Secretary of the U. S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inf orm the Air Force Strategy 2030\, and invited to speak at the Federal Info rmation Integrity R&D Interagency Working Group (IIRD IWG) briefing in 202 3. She is the lead of many multi-institution projects and tasks\, includin g the U.S. ARL projects on information fusion and knowledge networks const ruction\, DARPA ECOLE MIRACLE team\, DARPA KAIROS RESIN team and DARPA DEF T Tinker Bell team. She has coordinated the NIST TAC Knowledge Base Popula tion task 2010-2022. She was the associate editor for IEEE/ACM Transaction on Audio\, Speech\, and Language Processing\, and served as the Program C ommittee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJC NLP2022. She is elected as the North American Chapter of the Association f or Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DARPA\, NSF\, DoE\ , ARL\, IARPA\, AFRL\, DHS) and industry (Apple\, Amazon\, Google\, Facebo ok\, Bosch\, IBM\, Disney).
DTSTART;TZID=America/New_York:20240405T120000 DTEND;TZID=America/New_York:20240405T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, Maryland 21218 SEQUENCE:0 SUMMARY:Heng Ji (University of Illinois Urbana-Champaign) “SmartBook: an AI Prophetess for Disaster Reporting and Forecasting” URL:https://www.clsp.jhu.edu/events/heng-ji-university-of-illinois-urbana-c hampaign-smartbook-an-ai-prophetess-for-disaster-reporting-and-forecasting / X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Ji END:VEVENT END:VCALENDAR