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:20240328T225740Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.\nBiography\n\nDamianos Karakos has been at Raytheon BBN for the pas t nine years\, where he is currently a Senior Principal Engineer\, Researc h. Before that\, he was research faculty at Johns Hopkins University. He h as worked on several Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety of HLT-rela ted topics (e.g.\, speech recognition\, speech activity detection\, keywor d search\, information retrieval). He has published more than 60 peer-revi ewed papers. His research interests lie at the intersection of human langu age technology and machine learning\, with an emphasis on statistical meth ods. He obtained a PhD in Electrical Engineering from the University of Ma ryland\, College Park\, in 2002. DTSTART;TZID=America/New_York:20210924T120000 DTEND;TZID=America/New_York:20210924T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Damianos Karakos (Raytheon BBN) “The Raytheon BBN Cross-lingual Inf ormation Retrieval System developed under the IARPA MATERIAL Program” URL:https://www.clsp.jhu.edu/events/damianos-karakos/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nRaytheon BBN participated in the IARPA MATERIAL progr am\, whose objective is to enable rapid development of language-independen t methods for cross-lingual information retrieval (CLIR). The challenging CLIR task of retrieving documents written (or spoken) in one language so t hat they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the MATERIAL program: limited tr aining data for automatic speech recognition and machine translation\, sca nt lexical resources\, non-standardized orthography\, etc. Furthermore\, t he format of the queries and the “Query-Weighted Value” performance measur e are non-standard and not previously studied in the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successf ul at addressing the above challenges and unique characteristics of the pr ogram.
\nBiography
\nDamianos Karakos has been at Raytheon BBN for the past nine years\, wh ere he is currently a Senior Principal Engineer\, Research. Before that\, he was research faculty at Johns Hopkins University. He has worked on seve ral Government projects (e.g.\, DARPA GALE\, DARPA RATS\, IARPA BABEL\, IA RPA MATERIAL\, IARPA BETTER) and on a variety of HLT-related topics (e.g.\ , speech recognition\, speech activity detection\, keyword search\, inform ation retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of human language technology an d machine learning\, with an emphasis on statistical methods. He obtained a PhD in Electrical Engineering from the University of Maryland\, College Park\, in 2002.
\n\n
Abstr act
\nLarge Language Models (LLMs) have demonstrated remark able capabilities across various domains. However\, it is still very chall enging to build highly-reliable applications with LLMs that support specia lized use cases. LLMs trained on web data often excel at capturing general language patterns\, but they could struggle to support specialized domain s and personalized user needs. Moreover\, LLMs can produce errors that are deceptively plausible\, making them potentially dangerous for high-trust scenarios. In this talk\, I will discuss some of our recent efforts in add ressing these challenges with data-efficient tuning methods and a novel fa ctuality evaluation framework. Specifically\, my talk will focus on buildi ng multilingual applications\, one crucial use case often characterized by limited tuning and evaluation data.
\nBio
\nXinyi(Cindy) Wang is a research scientist at Google DeepMind working on La rge Language Models(LLM) and its application to generative question-answer ing. She has worked on multilingual instruction-tuning for Gemini and mult ilingual generative models used in Google search. Before Google DeepMind\, Cindy Wang obtained her PhD degree in Language Technologies at Carnegie M ellon University. During her PhD\, she mainly worked on developing data-ef ficient natural language processing~(NLP) systems. She has made several co ntributions in data selection\, data representation\, and model adaptation for multilingual NLP.
\n X-TAGS;LANGUAGE=en-US:2024\,March\,Wang END:VEVENT END:VCALENDAR