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:20240329T083034Z 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
\nAdversarial atta cks deceive neural network systems by adding carefully crafted perturbatio ns to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security threat to the state-of-the-art speech and speaker recognition systems\, making it vital to propose countermeasures against t hem. In this talk\, we focus on 1) classification of a given adversarial a ttack into attack algorithm type\, threat model type\, and signal-to-adver sarial-noise ratios\, 2) developing a novel speech denoising solution to f urther improve the classification performance.
\nO ur proposed approach uses an x-vector network as a signature extractor to get embeddings\, which we call signatures. These signatures contain inform ation about the attack and can help classify different attack algorithms\, threat models\, and signal-to-adversarial-noise ratios. We demonstrate th e transferability of such signatures to other tasks. In particular\, a sig nature extractor trained to classify attacks against speaker identificatio n can also be used to classify attacks against speaker verification and sp eech recognition. We also show that signatures can be used to detect unkno wn attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature extractor by making the job of the signature ext ractor easier by removing the clean signal from the adversarial example (w hich consists of clean signal+perturbation). We train our signature extrac tor using adversarial perturbation. At inference time\, we use a time-doma in denoiser to obtain adversarial perturbation from adversarial examples. Using our improved approach\, we show that common attacks in the literatur e (Fast Gradient Sign Method (FGSM)\, Projected Gradient Descent (PGD)\, C arlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We al so detect unknown attacks with an equal error rate (EER) of about 9%\, whi ch is very promising.
DTSTART;TZID=America/New_York:20220304T120000 DTEND;TZID=America/New_York:20220304T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Sonal Joshi “Classify and Detect Adversarial Atta cks Against Speaker and Speech Recognition Systems” URL:https://www.clsp.jhu.edu/events/student-seminar-sonal-joshi/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23312@www.clsp.jhu.edu DTSTAMP:20240329T083034Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nAdvanced neural language m odels have grown ever larger and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretabi lity. The black-box nature of deep neural networks blocks humans from unde rstanding them\, as well as trusting and using them in real-world applicat ions. This talk will introduce interpretation techniques that bridge the g ap between humans and models for developing trustworthy natural language p rocessing
\n (NLP). I will first show how to explain black-box models and evaluate their explanations for understanding their p rediction behavior. Then I will introduce how to improve the interpretabil ity of neural language models by making their decision-making transparent and rationalized. Finally\, I will discuss how to diagnose and improve mod els (e.g.\, robustness) through the lens of explanations. I will conclude with future research directions that are centered around model interpretab ility and committed to facilitating communications and interactions betwee n intelligent machines\, system developers\, and end users for long-term t rustworthy AI.Biography
\nHanjie Chen is a Ph.D. candidate in Computer Science at the University of Virginia\, advis ed by Prof. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Na tural Language Processing (NLP)\, and
DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT BEGIN:VEVENT UID:ai1ec-23882@www.clsp.jhu.edu DTSTAMP:20240329T083034Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: Interpretabl e Machine Learning. She develops interpretation techniques to explain neur al language models and make their prediction behavior transparent and reli able. She is a recipient of the Carlos and Esther Farrar Fellowship and th e Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selec ted by the National Center for Women & Information Technology (NCWIT) Coll egiate Award Finalist 2021. She (as the primary instructor) co-designed an d taught the course\, Interpretable Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teaching Award and University-wide Graduate T eaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mxAbstract
\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-24511@www.clsp.jhu.edu DTSTAMP:20240329T083034Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240412T120000 DTEND;TZID=America/New_York:20240412T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sonal Joshi (JHU) URL:https://www.clsp.jhu.edu/events/sonal-joshi-jhu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Joshi END:VEVENT END:VCALENDAR