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-20723@www.clsp.jhu.edu DTSTAMP:20240329T052917Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nText simplification aims t o help audiences read and understand a piece of text through lexical\, syn tactic\, and discourse modifications\, while remaining faithful to its cen tral idea and meaning. Thanks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focu ses on sentence simplification\, transforming original\, more complex sent ences into simplified versions. In this talk\, I present new frontiers tha t focus on discourse operations. First\, we consider the challenging task of simplifying highly technical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinic al topics. We then propose a new metric to quantify stylistic differentiat es between the two\, and models for paragraph-level simplification. Second \, we present the first data-driven study of inserting elaborations and ex planations during simplification\, and illustrate the richness and complex ities of this phenomenon.
\nBiography
\nAbstract
\n\n\n\n\nAutomatic discovery of phon e or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC)\, where the model learns representations by predicting the next frame given past context. However\, CPC only looks at the audio signal’s structure at the frame level. The speech structure exists beyond frame-level\, i.e.\, a t phone level or even higher. We propose a segmental contrastive predictiv e coding (SCPC) framework to learn from the signal structure at both the f rame and phone levels.\n\n\nSCPC is a hierarchical model with three stages trained in an end-to-end m anner. In the first stage\, the model predicts future feature frames and e xtracts frame-level representation from the raw waveform. In the second st age\, a differentiable boundary detector finds variable-length segments. I n the last stage\, the model predicts future segments to learn segment rep resentations. Experiments show that our model outperforms existing phone a nd word segmentation methods on TIMIT and Buckeye datasets.
Abstract
\nWhile GPT models have shown impressive performance on summa rization and open-ended text generation\, it’s important to assess their a bilities on more constrained text generation tasks that require significan t and diverse rewritings. In this talk\, I will discuss the challenges of evaluating systems that are highly competitive and perform close to humans on two such tasks: (i) paraphrase generation and (ii) text simplification . To address these challenges\, we introduce an interactive Rank-and-Rate evaluation framework. Our results show that GPT-3.5 has made a major step up from fine-tuned T5 in paraphrase generation\, but still lacks the diver sity and creativity of humans who spontaneously produce large quantities o f paraphrases.
\nAdditionally\, we demonstrate that GPT-3.5 performs similarly to a sin gle human in text simplification\, which makes it difficult for existing a utomatic evaluation metrics to distinguish between the two. To overcome th is shortcoming\, we propose LENS\, a learnable evaluation metric that outp erforms SARI\, BERTScore\, and other existing methods in both automatic ev aluation and minimal risk decoding for text generation.
\nBiography
\nWei Xu is an assistant professor in the School of Interactive Com puting at the Georgia Institute of Technology\, where she is also affiliat ed with the new NSF AI CARING Institute and Machine Learning Center. She r eceived her Ph.D. in Computer Science from New York University and her B.S . and M.S. from Tsinghua University. Xu’s research interests are in natura l language processing\, machine learning\, and social media\, with a focus on text generation\, stylistics\, robustness and controllability of machi ne learning models\, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award\, CrowdFlower AI for Everyone Award\, Criteo Faculty Research Award\, and Best Paper Award at COLING’18. She has also received funds from DARPA and IARPA. She is an elected member of the NAACL executive board and regularly serves as a senior area chair for AI/ NLP conferences.
DTSTART;TZID=America/New_York:20230224T120000 DTEND;TZID=America/New_York:20230224T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Wei Xu (Georgia Tech) “GPT-3 vs Humans: Rethinking Evaluation of Na tural Language Generation” URL:https://www.clsp.jhu.edu/events/wei-xu-georgia-tech/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Xu END:VEVENT BEGIN:VEVENT UID:ai1ec-23882@www.clsp.jhu.edu DTSTAMP:20240329T052917Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nLarge language models (LLM s) have demonstrated incredible power\, but they also possess vulnerabilit ies that can lead to misuse and potential attacks. In this presentation\, we will address two fundamental questions regarding the responsible utiliz ation of LLMs: (1) How can we accurately identify AI-generated text? (2) W hat measures can safeguard the intellectual property of LLMs? We will intr oduce two recent watermarking techniques designed for text and models\, re spectively. Our discussion will encompass the theoretical underpinnings th at ensure the correctness of watermark detection\, along with robustness a gainst evasion attacks. Furthermore\, we will showcase empirical evidence validating their effectiveness. These findings establish a solid technical groundwork for policymakers\, legal professionals\, and generative AI pra ctitioners alike.
\nBiography
\nLei Li is an Assistant Professor in Language Technology Institute at Carnegie Mellon Un iversity. He received Ph.D. from Carnegie Mellon University School of Comp uter Science. He is a recipient of ACL 2021 Best Paper Award\, CCF Young E lite Award in 2019\, CCF distinguished speaker in 2017\, Wu Wen-tsün AI pr ize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a facul ty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI La b in 2016 and led its research in NLP\, ML\, Robotics\, and Drug Discovery . He launched ByteDance’s machine translation system VolcTrans and AI writ ing system Xiaomingbot\, serving one billion users.
DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT END:VCALENDAR