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-23894@www.clsp.jhu.edu DTSTAMP:20240329T110403Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.\nBiography\nMark Dredze is the John C Malone Professor of Computer Science at Johns Hopkins University and the Director of Research (Foundations of AI) for the JHU AI-X Foundry. He deve lops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.\nProf. Dredze is affiliated with the Malone Center for Engineering in Healthcare\, the Cent er for Language and Speech Processing\, among others. He holds a joint app ointment in the Biomedical Informatics & Data Science Section (BIDS)\, und er the Department of Medicine (DOM)\, Division of General Internal Medicin e (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009. DTSTART;TZID=America/New_York:20230918T120000 DTEND;TZID=America/New_York:20230918T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Dredze (Johns Hopkins University) “BloombergGPT: A Large Langu age Model for Finance” URL:https://www.clsp.jhu.edu/events/mark-dredze-johns-hopkins-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nThe use of NLP in the realm of financial technology i s broad and complex\, with applications ranging from sentiment analysis an d named entity recognition to question answering. Large Language Models (L LMs) have been shown to be effective on a variety of tasks\; however\, no LLM specialized for the financial domain has been reported in the literatu re. In this work\, we present BloombergGPT\, a 50 billion parameter langua ge model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, p erhaps the largest domain-specific dataset yet\, augmented with 345 billio n tokens from general-purpose datasets. We validate BloombergGPT on stand ard LLM benchmarks\, open financial benchmarks\, and a suite of internal b enchmarks that most accurately reflect our intended usage. Our mixed datas et training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general L LM benchmarks. Additionally\, we explain our modeling choices\, training p rocess\, and evaluation methodology.
\nBiography
\nMark Dredze is the John C Malone Professor of Computer Science at Jo hns Hopkins University and the Director of Research (Foundations of AI) fo r the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.
\nProf. Dredze is affiliated with the Malone Center fo r Engineering in Healthcare\, the Center for Language and Speech Processin g\, among others. He holds a joint appointment in the Bio medical Informatics & Data Science Section (< span class='il'>BIDS)\, under the Department of Medicine (DOM)\, Di vision of General Internal Medicine (GIM) in the School of Medicine. He ob tained his PhD from the University of Pennsylvania in 2009.
\n HTML> X-TAGS;LANGUAGE=en-US:2023\,Dredze\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24481@www.clsp.jhu.edu DTSTAMP:20240329T110403Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.\nBiography\nEunsol Choi is an assistant profess or in the Computer Science department at the University of Texas at Austin . Prior to UT\, she spent a year at Google AI as a visiting researcher. He r research area spans natural language processing and machine learning. Sh e is particularly interested in interpreting and reasoning about text in a dynamic real world context. She is a recipient of a Facebook research fel lowship\, Google faculty research award\, Sony faculty award\, and an outs tanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics and comp uter science from Cornell University. DTSTART;TZID=America/New_York:20240315T120000 DTEND;TZID=America/New_York:20240315T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21209 SEQUENCE:0 SUMMARY:Eunsol Choi (University of Texas at Austin) “Knowledge-Rich Languag e Systems in a Dynamic World” URL:https://www.clsp.jhu.edu/events/eunsol-choi-university-of-texas-at-aust in-knowledge-rich-language-systems-in-a-dynamic-world/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.
\nBiography
\nEunsol Choi is an assistant professor in the Computer Scie nce department at the University of Texas at Austin. Prior to UT\, she spe nt a year at Google AI as a visiting researcher. Her research area spans n atural language processing and machine learning. She is particularly inter ested in interpreting and reasoning about text in a dynamic real world con text. She is a recipient of a Facebook research fellowship\, Google facult y research award\, Sony faculty award\, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from Unive rsity of Washington and B.A in mathematics and computer science from Corne ll University.
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