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Foundation Models for the Real World.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Foundation Models for the Real World./
作者:
Tamkin, Alexander.
面頁冊數:
1 online resource (179 pages)
附註:
Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:
Dissertations Abstracts International85-04B.
標題:
Active learning. -
電子資源:
click for full text (PQDT)
ISBN:
9798380485869
Foundation Models for the Real World.
Tamkin, Alexander.
Foundation Models for the Real World.
- 1 online resource (179 pages)
Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Thesis (Ph.D.)--Stanford University, 2023.
Includes bibliographical references
Foundation models are quickly moving from their origins in the lab into real world deployment and use. In this thesis, I discuss two connected lines of research that work towards bridging this gap, so that foundation models can be fruitfully used in real-world settings, e.g. in engineering, medicine, or the sciences. The first is making models more domain-agnostic: while techniques for training foundation models were developed for language and vision domains, we show that simple techniques can generalize these approaches to work across at least twelve different domains. The second is making models more useful in cases of task ambiguity, where the user's desired task may be vague or not-perfectly specified, as is often the case in real-world settings. Here we show how to measure and improve the performance of foundation models under task ambiguity, and explore how models themselves can aid in the process of disambiguating user intent. We close by discussing future directions and the broader outlook of challenges and opportunities ahead.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798380485869Subjects--Topical Terms:
555387
Active learning.
Index Terms--Genre/Form:
554714
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Foundation Models for the Real World.
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Foundation models are quickly moving from their origins in the lab into real world deployment and use. In this thesis, I discuss two connected lines of research that work towards bridging this gap, so that foundation models can be fruitfully used in real-world settings, e.g. in engineering, medicine, or the sciences. The first is making models more domain-agnostic: while techniques for training foundation models were developed for language and vision domains, we show that simple techniques can generalize these approaches to work across at least twelve different domains. The second is making models more useful in cases of task ambiguity, where the user's desired task may be vague or not-perfectly specified, as is often the case in real-world settings. Here we show how to measure and improve the performance of foundation models under task ambiguity, and explore how models themselves can aid in the process of disambiguating user intent. We close by discussing future directions and the broader outlook of challenges and opportunities ahead.
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