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Behavior-Bound Machine Learning
Behavior-Bound Machine Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153056
- ISBN
- 9798346389989
- DDC
- 519.54
- 서명/저자
- Behavior-Bound Machine Learning
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 176 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
- 주기사항
- Advisor: Jurafsky, Dan.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약Machine learning is often characterized as a sterile process, but in reality, it is shaped by workers, firms, states, and many other actors. In fact, it is often the behavior of these actors that principally determines what machine learning can accomplish in the real world, even more so than traditional concerns like the amount of memory or compute. Therefore, I propose that much in the way we characterize processes as being compute-bound or memory-bound, we should also think of them as being behavior-bound. By then formalizing real-world behavior, we can create machine learning pipelines that are compatible with actual actors and not just idealized ones. For inspiration, I turn to economics, a field that has undergone a similar revolution over the past half-century, moving away from a conception of people as perfectly rational agents making optimal decisions to studying the complex reality of how they actually behave. I first explain how conflicting interests between actors leads to datasets being much simpler than the problems they purport to reflect, and how we can diagnose this using the theory of usable information. I then use this framework to create one of the largest datasets for aligning large language models with human feedback. The next chapter argues that the methods used for aligning generative models are uniquely effective because they capture systematic biases in the way that humans themselves make decisions, and that human-aware alignment methods can be cheaper, faster, and more robust. Lastly, I present an evaluation-as-a-service platform that evaluates these models in a cost-sensitive way instead of treating them as if they exist in a vacuum. Together, these contributions lay the foundation for conceptualizing machine learning as a part of a larger sociotechnical whole.
- 일반주제명
- Standard scores
- 일반주제명
- Large language models
- 일반주제명
- Statistics
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aEthayarajh, Kawin.
■24510▼aBehavior-Bound Machine Learning
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a176 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-05, Section: B.
■500 ▼aAdvisor: Jurafsky, Dan.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aMachine learning is often characterized as a sterile process, but in reality, it is shaped by workers, firms, states, and many other actors. In fact, it is often the behavior of these actors that principally determines what machine learning can accomplish in the real world, even more so than traditional concerns like the amount of memory or compute. Therefore, I propose that much in the way we characterize processes as being compute-bound or memory-bound, we should also think of them as being behavior-bound. By then formalizing real-world behavior, we can create machine learning pipelines that are compatible with actual actors and not just idealized ones. For inspiration, I turn to economics, a field that has undergone a similar revolution over the past half-century, moving away from a conception of people as perfectly rational agents making optimal decisions to studying the complex reality of how they actually behave. I first explain how conflicting interests between actors leads to datasets being much simpler than the problems they purport to reflect, and how we can diagnose this using the theory of usable information. I then use this framework to create one of the largest datasets for aligning large language models with human feedback. The next chapter argues that the methods used for aligning generative models are uniquely effective because they capture systematic biases in the way that humans themselves make decisions, and that human-aware alignment methods can be cheaper, faster, and more robust. Lastly, I present an evaluation-as-a-service platform that evaluates these models in a cost-sensitive way instead of treating them as if they exist in a vacuum. Together, these contributions lay the foundation for conceptualizing machine learning as a part of a larger sociotechnical whole.
■590 ▼aSchool code: 0212.
■650 4▼aStandard scores
■650 4▼aLarge language models
■650 4▼aStatistics
■690 ▼a0800
■690 ▼a0463
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-05B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164865▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
