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-20716@www.clsp.jhu.edu DTSTAMP:20240328T084808Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nOver the last few years\, deep neural models have tak en over the field of natural language processing (NLP)\, brandishing great improvements on many of its sequence-level tasks. But the end-to-end natu re of these models makes it hard to figure out whether the way they repres ent individual words aligns with how language builds itself from the botto m up\, or how lexical changes in register and domain can affect the untest ed aspects of such representations.\nIn this talk\, I will present NYTWIT\ , a dataset created to challenge large language models at the lexical leve l\, tasking them with identification of processes leading to the formation of novel English words\, as well as with segmentation and recovery of the specific subclass of novel blends. I will then present XRayEmb\, a method which alleviates the hardships of processing these novelties by fitting a character-level encoder to the existing models’ subword tokenizers\; and conclude with a discussion of the drawbacks of current tokenizers’ vocabul ary creation schemes.\nBiography\nYuval Pinter is a Senior Lecturer in the Department of Computer Science at Ben-Gurion University of the Negev\, fo cusing on natural language processing. Yuval got his PhD at the Georgia In stitute of Technology School of Interactive Computing as a Bloomberg Data Science PhD Fellow. Before that\, he worked as a Research Engineer at Yaho o Labs and as a Computational Linguist at Ginger Software\, and obtained a n MA in Linguistics and a BSc in CS and Mathematics\, both from Tel Aviv U niversity. Yuval blogs (in Hebrew) about language matters on Dagesh Kal. DTSTART;TZID=America/New_York:20210910T120000 DTEND;TZID=America/New_York:20210910T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD SEQUENCE:0 SUMMARY:Yuval Pinter (Ben-Gurion University – Virtual Visit) “Challenging a nd Adapting NLP Models to Lexical Phenomena” URL:https://www.clsp.jhu.edu/events/yuval-pinter/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nOver the last few years\, deep neural models have tak en over the field of natural language processing (NLP)\, brandishing great improvements on many of its sequence-level tasks. But the end-to-end natu re of these models makes it hard to figure out whether the way they repres ent individual words aligns with how language builds itself from the botto m up\, or how lexical changes in register and domain can affect the untest ed aspects of such representations.
\nIn this talk\, I will present NYTWIT\, a dataset created to challenge large language models at the lexic al level\, tasking them with identification of processes leading to the fo rmation of novel English words\, as well as with segmentation and recovery of the specific subclass of novel blends. I will then present XRayEmb\, a method which alleviates the hardships of processing these novelties by fi tting a character-level encoder to the existing models’ subword tokenizers \; and conclude with a discussion of the drawbacks of current tokenizers’ vocabulary creation schemes.
\nBiography
\nYuval Pinter
is a Senior Lecturer in the Department of Computer Science at Ben-Gurion
University of the Negev\, focusing on natural language processing. Yuval got his PhD at the Georgia Institute of Tec
hnology School of Interactive Computing as a Bloomberg Data Science PhD Fe
llow. Before that\, he worked as a Research Engineer at Yahoo Labs and as
a Computational Linguist at Ginger Software\, and obtained an MA in Lingui
stics and a BSc in CS and Mathematics\, both from Tel Aviv University.
Abstr 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.
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