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Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104742
- ISBN
- 9798290651934
- DDC
- 006.696
- 저자명
- Liang, Weixin.
- 서명/저자
- Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
- 발행사항
- [Sl] : Stanford University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 229 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Kundaje, Anshul;Zou, James.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2025.
- 초록/해제
- 요약Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.
- 일반주제명
- Deepfake
- 일반주제명
- Writing
- 일반주제명
- Large language models
- 일반주제명
- Chatbots
- 일반주제명
- Semantics
- 일반주제명
- Computer engineering
- 키워드
- Job postings
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a006.696
■1001 ▼aLiang, Weixin.
■24510▼aComputational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
■260 ▼a[Sl]▼bStanford University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a229 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Kundaje, Anshul;Zou, James.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2025.
■520 ▼aLarge language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.
■590 ▼aSchool code: 0212.
■650 4▼aDeepfake
■650 4▼aWriting
■650 4▼aLarge language models
■650 4▼aChatbots
■650 4▼aSemantics
■650 4▼aComputer engineering
■653 ▼aLarge language models
■653 ▼aJob postings
■690 ▼a0464
■690 ▼a0800
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358718▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
