{"id":31009,"date":"2022-12-02T06:00:00","date_gmt":"2022-12-02T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=31009"},"modified":"2023-06-23T12:38:31","modified_gmt":"2023-06-23T19:38:31","slug":"research-highlights-rr-metric-guided-adversarial-sentence-generation","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/","title":{"rendered":"Research Highlights: R&R: Metric-guided Adversarial Sentence Generation"},"content":{"rendered":"
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Large language models are a hot topic in AI research right now. But there\u2019s a hotter, more significant problem looming: we might run out of data to train them on … as early as 2026<\/a>. <\/p>\n\n\n\n

Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab<\/a> may have found the solution: in their paper on Rewrite and Rollback (\u201cR&R: Metric-Guided Adversarial Sentence Generation\u201d) just published in the Findings of AACL-IJCNLP<\/em><\/a>,<\/em> an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type<\/em> of data to test and train language models on. (While there is a plethora of low-quality data available, we shouldn\u2019t be training language models on social media posts and comments from fringe forums \u2026 you could probably already guess which models have.)<\/em><\/p>\n\n\n\n

Here is the peer-reviewed paper for your reference: https:\/\/aclanthology.org\/2022.findings-aacl.41.pdf<\/a><\/p>\n\n\n\n

About Kalyan Veeramachaneni<\/strong><\/p>\n\n\n\n

Kalyan Veeramachaneni<\/em><\/a> is a principal research scientist at the <\/em>MIT Schwarzman College of Computing<\/em><\/a>. In 2015, he founded<\/em> MIT\u2019s Data-to-AI Lab<\/em><\/a> (part of MIT\u2019s <\/em>LIDS<\/em><\/a>) where he leads a team of like-minded scientists in the drive to #AIforGood that combines Big Data + Human Interactions + Impactful Domains (machine + human + positive societal impact). His research focuses on building large-scale AI systems that work alongside humans, continuously learning from data that generate and integrate predictions into \u201caugmented\u201d human decision-making. The algorithms, systems and open-source software developed by the MIT Data-to-AI (DAI) Lab are deployed for applications in the financial, healthcare, educational and energy sectors. Prior to leading the MIT DAI Lab, he was a research scientist at<\/em> MIT CSAIL<\/em><\/a>.<\/em> Kalyan co-founded three AI-focused businesses:<\/em> DataCebo<\/em><\/a>, the commercial spin-off from the MIT DAI Lab\u2019s <\/em>Synthetic Data Vault (SDV)<\/em><\/a> providing businesses the opportunity to utilize synthetic data to test and train their machine learning models; <\/em>Feature Labs<\/em><\/a>, a data science automation company acquired by Alteryx (NYSE:AYX); and<\/em> PatternEx<\/em><\/a>, a cybersecurity company that combined the power of humans and machines into an AI system that detects cyber threats at scale and in real time, acquired by Corelight. <\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

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Join us on Facebook: https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Large language models are a hot topic in AI research right now. But there\u2019s a hotter, more significant problem looming: we might run out of data to train them on … as early as 2026.\u00a0Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab may have found the solution: in their new paper on Rewrite and Rollback (\u201cR&R: Metric-Guided Adversarial Sentence Generation\u201d), an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type of data to test and train language models on.<\/p>\n","protected":false},"author":10513,"featured_media":23389,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,180,67,268,56,84,1303,1],"tags":[437,324,264,1248,635,96],"acf":[],"yoast_head":"\nResearch Highlights: R&R: Metric-guided Adversarial Sentence Generation - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Highlights: R&R: Metric-guided Adversarial Sentence Generation - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Large language models are a hot topic in AI research right now. But there\u2019s a hotter, more significant problem looming: we might run out of data to train them on ... as early as 2026.\u00a0Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab may have found the solution: in their new paper on Rewrite and Rollback (\u201cR&R: Metric-Guided Adversarial Sentence Generation\u201d), an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type of data to test and train language models on.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2022-12-02T14:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-23T19:38:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/10\/NLP_shutterstock_299138114.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Editorial Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Editorial Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/\",\"url\":\"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/\",\"name\":\"Research Highlights: R&R: Metric-guided Adversarial Sentence Generation - 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They\u2019ve been building it for three years\u2014AER has been learning and extracting intelligence for signals and\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":28658,"url":"https:\/\/insidebigdata.com\/2022\/03\/11\/research-highlights-generative-adversarial-networks\/","url_meta":{"origin":31009,"position":1},"title":"Research Highlights: Generative Adversarial Networks","date":"March 11, 2022","format":false,"excerpt":"In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It's important to keep connected with the research arm of the field in order to see where we're headed. In this edition,\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2022\/03\/Research_highlights_1.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":28141,"url":"https:\/\/insidebigdata.com\/2022\/01\/06\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-december-2021\/","url_meta":{"origin":31009,"position":2},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 December 2021","date":"January 6, 2022","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":22953,"url":"https:\/\/insidebigdata.com\/2019\/07\/18\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-june-2019\/","url_meta":{"origin":31009,"position":3},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 June 2019","date":"July 18, 2019","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":24604,"url":"https:\/\/insidebigdata.com\/2020\/06\/17\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-may-2020\/","url_meta":{"origin":31009,"position":4},"title":"Best of arXiv.org for AI, Machine Learning, and Deep Learning \u2013 May 2020","date":"June 17, 2020","format":false,"excerpt":"In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning \u2013 from disciplines including statistics, mathematics and computer science \u2013 and provide you with a useful \u201cbest of\u201d list for the\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/12\/arxiv.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":33083,"url":"https:\/\/insidebigdata.com\/2023\/08\/08\/netspi-debuts-ml-ai-penetration-testing-a-holistic-approach-to-securing-machine-learning-models-and-llm-implementations\/","url_meta":{"origin":31009,"position":5},"title":"NetSPI Debuts ML\/AI Penetration Testing, a Holistic Approach to Securing Machine Learning Models and LLM Implementations","date":"August 8, 2023","format":false,"excerpt":"NetSPI, the global leader in offensive security, today debuted its ML\/AI Pentesting solution to bring a more holistic and proactive approach to safeguarding machine learning model implementations. 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