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-21259@www.clsp.jhu.edu DTSTAMP:20240329T160052Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.\nBiography\nTom McCoy is a PhD c andidate in the Department of Cognitive Science at Johns Hopkins Universit y. As an undergraduate\, he studied computational linguistics at Yale. His research combines natural language processing\, cognitive science\, and m achine learning to study how we can achieve robust generalization in model s of language\, as this remains one of the main areas where current AI sys tems fall short. In particular\, he focuses on inductive biases and repres entations of linguistic structure\, since these are two of the major compo nents that determine how learners generalize to novel types of input. DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Sci ence at Johns Hopkins University. As an undergraduate\, he studied computa tional linguistics at Yale. His research combines natural language process ing\, cognitive science\, and machine learning to study how we can achieve robust generalization in models of language\, as this remains one of the main areas where current AI systems fall short. In particular\, he focuses on inductive biases and representations of linguistic structure\, since t hese are two of the major components that determine how learners generaliz e to novel types of input.
\n X-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-23306@www.clsp.jhu.edu DTSTAMP:20240329T160052Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile large language models have advanced the state-o f-the-art in natural language processing\, these models are trained on lar ge-scale datasets\, which may include harmful information. Studies have sh own that as a result\, the models exhibit social biases and generate misin formation after training. In this talk\, I will discuss my work on analyzi ng and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I will first describe my researc h in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part investigates the trus tworthiness of models through the memorization and subsequent generation o f conspiracy theories. I will end my talk with recent work in AI safety re garding text that may lead to physical harm.\nBiography\nSharon is a 5th-y ear Ph.D. candidate at the University of California\, Santa Barbara\, wher e she is advised by Professor William Wang. Her research interests lie in natural language processing\, with a focus on Responsible AI. Sharon’s res earch spans the subareas of fairness\, trustworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LREC. She has spent summers inter ning at AWS\, Meta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI. DTSTART;TZID=America/New_York:20230206T120000 DTEND;TZID=America/New_York:20230206T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sharon Levy (University of California\, Santa Barbara) “Responsible AI via Responsible Large Language Models” URL:https://www.clsp.jhu.edu/events/sharon-levy-university-of-california-sa nta-barbara-responsible-ai-via-responsible-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nWhile large language models have advanced the state-o f-the-art in natural language processing\, these models are trained on lar ge-scale datasets\, which may include harmful information. Studies have sh own that as a result\, the models exhibit social biases and generate misin formation after training. In this talk\, I will discuss my work on analyzi ng and interpreting the risks of large language models across the areas of fairness\, trustworthiness\, and safety. I will first describe my researc h in the detection of dialect bias between African American English (AAE) vs. Standard American English (SAE). The second part investigates the trus tworthiness of models through the memorization and subsequent generation o f conspiracy theories. I will end my talk with recent work in AI safety re garding text that may lead to physical harm.
\nBiography
\nSharon is a 5th-year Ph.D. candidate at the University of Ca lifornia\, Santa Barbara\, where she is advised by Professor William Wang. Her research interests lie in natural language processing\, with a focus on Responsible AI. Sharon’s research spans the subareas of fairness\, trus tworthiness\, and safety\, with publications in ACL\, EMNLP\, WWW\, and LR EC. She has spent summers interning at AWS\, Meta\, and Pinterest. Sharon is a 2022 EECS Rising Star and a current recipient of the Amazon Alexa AI Fellowship for Responsible AI.
\n X-TAGS;LANGUAGE=en-US:2023\,February\,Levy END:VEVENT END:VCALENDAR