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-20730@www.clsp.jhu.edu DTSTAMP:20240329T082344Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nRaytheon BBN participated in the IARPA MATERIAL program\, whose objective is to enable rapid develop ment of language-independent methods for cross-lingual information retriev al (CLIR). The challenging CLIR task of retrieving documents written (or s poken) in one language so that they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the M ATERIAL program: limited training data for automatic speech recognition an d machine translation\, scant lexical resources\, non-standardized orthogr aphy\, etc. Furthermore\, the format of the queries and the “Query-Weighte d Value” performance measure are non-standard and not previously studied i n the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successful at addressing the above challenges and uniqu e characteristics of the program.
\nBiography
\nDamianos Karakos has been at Raytheon BBN f or the past nine years\, where he is currently a Senior Principal Engineer \, Research. Before that\, he was research faculty at Johns Hopkins Univer sity. He has worked on several Government projects (e.g.\, DARPA GALE\, DA RPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety o f HLT-related topics (e.g.\, speech recognition\, speech activity detectio n\, keyword search\, information retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of h uman language technology and machine learning\, with an emphasis on statis tical methods. He obtained a PhD in Electrical Engineering from the Univer sity of Maryland\, College Park\, in 2002.
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Abstract
\nThe use of NLP in the real m of financial technology is broad and complex\, with applications ranging from sentiment analysis and named entity recognition to question answerin g. Large Language Models (LLMs) have been shown to be effective on a varie ty of tasks\; however\, no LLM specialized for the financial domain has be en reported in the literature. In this work\, we present BloombergGPT\, a 50 billion parameter language model that is trained on a wide range of fin ancial data. We construct a 363 billion token dataset based on Bloomberg’s extensive data sources\, perhaps the largest domain-specific dataset yet\ , augmented with 345 billion tokens from general-purpose datasets. We val idate BloombergGPT on standard LLM benchmarks\, open financial benchmarks\ , and a suite of internal benchmarks that most accurately reflect our inte nded usage. Our mixed dataset training leads to a model that outperforms e xisting models on financial tasks by significant margins without sacrifici ng performance on general LLM benchmarks. Additionally\, we explain our mo deling choices\, training process\, and evaluation methodology.
\nBiography
Mark Dredze is the John C Malone Professo r of Computer Science at Johns Hopkins University and the Director of Rese arch (Foundations of AI) for the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores app lications to public health and medicine.
\nProf. Dredze is affiliate d with the Malone Center for Engineering in Healthcare\, the Center for La nguage and Speech Processing\, among others. He holds a joint appointment in the Biomedical Informatics & Data Science Section (BIDS)\, under the Depart ment of Medicine (DOM)\, Division of General Internal Medicine (GIM) in th e School of Medicine. He obtained his PhD from the University of Pennsylva nia 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-TAGS;LANGUAGE=en-US:2023\,Dredze\,September END:VEVENT END:VCALENDAR