{"id":130692,"date":"2022-12-20T16:55:44","date_gmt":"2022-12-20T16:55:44","guid":{"rendered":"https:\/\/news.microsoft.com\/?p=447965"},"modified":"2022-12-20T16:55:44","modified_gmt":"2022-12-20T16:55:44","slug":"tapping-into-large-language-models-with-microsofts-turing-academic-program","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2022\/12\/20\/tapping-into-large-language-models-with-microsofts-turing-academic-program\/","title":{"rendered":"Tapping into Large Language Models with Microsoft\u2019s Turing Academic Program"},"content":{"rendered":"
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– By Eric Horvitz<\/a>, Chief Scientific Officer, and Saurabh Tiwary<\/a>, Corporate VP and Technical Fellow, Microsoft Turing <\/h5>\n

The AI research community is focusing attention on large language models (LLMs) given their impressive performance with difficult tasks and value in useful applications. The capabilities of the models have stimulated many research questions: How do these models work and what do they learn? How can we leverage their potential for new innovations and discoveries? How can we mitigate potentially harmful behaviors? These and many other questions are inspiring new and challenging directions for AI research and underscoring the need for different perspectives and proficiencies. 
 
To explore what\u2019s ahead for LLMs and discuss the ways industry, academia, and government could work together to advance understanding of these models, we organized a panel titled, \u201cTowards a Healthy Research Ecosystem for Large Language Models.\u201d We were joined by Ahmed Awadallah<\/a> from Microsoft Research, Erwin Gianchandani<\/a> from the National Science Foundation, and Percy Liang<\/a> from Stanford University who each brought fascinating insights and ideas for consideration. <\/p>\n

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A key part of the discussion is on the need to expand access to large language models. Building and experimenting with the largest models requires a great deal of data and computing resources, which are often beyond the reach of university-based teams. This challenge was our motivation for launching the Microsoft Turing Academic Program<\/a> (MS-TAP). Since 2021, we have sought to provide leading academic teams with access to some of the world’s largest language models. The program reflects our belief in the importance of having diverse and talented teams from academia working with these models.<\/p>\n

MS-TAP has supported multiple in-depth collaborations with partner universities. With deep engagement from researchers and domain experts in Microsoft Research, Microsoft Turing, and the Office of the Chief Scientific Officer, we work to better understand model behavior, identify novel applications, explore potential risks, develop mitigations, and improve future models. Participants receive unprecedented access to our 530B parameter Natural Language Generation model (T-NLGv2)<\/a>, Natural Language Representation model (T-NLRv5)<\/a>, and 2.5B parameter Universal Image Language Representation model (T-UILRv2)<\/a> and Azure compute resources to run experiments and evaluations.
 
MS-TAP Phase 1 involved collaborations with six universities on five projects (see below) and included numerous highlights like these papers: Was it \u201csaid\u201d or was it \u201cclaimed\u201d? How linguistic bias affects generative language models<\/em><\/a> (Brown University) and Invariant Language Modeling<\/em><\/a> (\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne) published at the EMNLP 2022<\/a> conference. More information including contributors and ongoing updates is available on the MS-TAP Phase 1 Collaborations<\/a> page.
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