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-22412@www.clsp.jhu.edu DTSTAMP:20240329T130357Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nDriven by the goal of eradicating language barriers o n a global scale\, machine translation has solidified itself as a key focu s of artificial intelligence research today. However\, such efforts have c oalesced around a small subset of languages\, leaving behind the vast majo rity of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe\, high-quality results\, all while ke eping ethical considerations in mind? In this talk\, I introduce No Langua ge Left Behind\, an initiative to break language barriers for low-resource languages. In No Language Left Behind\, we took on the low-resource langu age translation challenge by first contextualizing the need for translatio n support through exploratory interviews with native speakers. Then\, we c reated datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and tr aining improvements to counteract overfitting while training on thousands of tasks. Critically\, we evaluated the performance of over 40\,000 differ ent translation directions using a human-translated benchmark\, Flores-200 \, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achiev es an improvement of 44% BLEU relative to the previous state-of-the-art\, laying important groundwork towards realizing a universal translation syst em in an open-source manner.\nBiography\nAngela is a research scientist at Meta AI Research in New York\, focusing on supporting efforts in speech a nd language research. Recent projects include No Language Left Behind (htt ps://ai.facebook.com/research/no-language-left-behind/) and Universal Spee ch Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-tr anslation-hokkien/). Before translation\, Angela previously focused on res earch in on-device models for NLP and computer vision and text generation. DTSTART;TZID=America/New_York:20221118T120000 DTEND;TZID=America/New_York:20221118T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Hu man-Centered Machine Translation” URL:https://www.clsp.jhu.edu/events/angela-fan-facebook/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nDriven by the goal of eradicating language barriers o n a global scale\, machine translation has solidified itself as a key focu s of artificial intelligence research today. However\, such efforts have c oalesced around a small subset of languages\, leaving behind the vast majo rity of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe\, high-quality results\, all while ke eping ethical considerations in mind? In this talk\, I introduce No Langua ge Left Behind\, an initiative to break language barriers for low-resource languages. In No Language Left Behind\, we took on the low-resource langu age translation challenge by first contextualizing the need for translatio n support through exploratory interviews with native speakers. Then\, we c reated datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and tr aining improvements to counteract overfitting while training on thousands of tasks. Critically\, we evaluated the performance of over 40\,000 differ ent translation directions using a human-translated benchmark\, Flores-200 \, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achiev es an improvement of 44% BLEU relative to the previous state-of-the-art\, laying important groundwork towards realizing a universal translation syst em in an open-source manner.
\nBiography
\nAngela is a research scientist at Meta AI Research in Ne w York\, focusing on supporting efforts in speech and language research. R ecent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-be hind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation -hokkien/). Before translation\, Angela previously focused on research in on-device models for NLP and computer vision and text generation.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24465@www.clsp.jhu.edu DTSTAMP:20240329T130357Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\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 Large Language Models(LLM) and it s application to generative question-answering. She has worked on multilin gual instruction-tuning for Gemini and multilingual generative models used in Google search. Before Google DeepMind\, Cindy Wang obtained her PhD de gree in Language Technologies at Carnegie Mellon University. During her Ph D\, she mainly worked on developing data-efficient natural language proces sing~(NLP) systems. She has made several contributions in data selection\, data representation\, and model adaptation for multilingual NLP. DTSTART;TZID=America/New_York:20240308T120000 DTEND;TZID=America/New_York:20240308T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Cindy Wang (Google DeepMind) “Building Data-Efficient and Reliable Applications with Large Language Models” URL:https://www.clsp.jhu.edu/events/cindy-wang-google-deepmind-building-dat a-efficient-and-reliable-applications-with-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\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.
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