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Reading Minds: Social Intelligence and Large Language Models
Reading Minds: Social Intelligence and Large Language Models
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151123
- ISBN
- 9798384067948
- DDC
- 150
- 서명/저자
- Reading Minds: Social Intelligence and Large Language Models
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 184 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: A.
- 주기사항
- Advisor: Bergen, Benjamin.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Human social intelligence is one of the defining features of our species, however, its origins and mechanisms are not well understood. The advent of Large Language Models (LLMs)-which learn to produce text on the basis of statistical patterns in the distribution of words-both threaten the uniqueness of human social intelligence and promise opportunities to better understand it. In this dissertation, I evaluate the extent to which distributional information learned by LLMs allows them to approximate human behavior on tasks that appear to require social intelligence. First, I compare human and LLM responses in experiments designed to measure theory of mind-the ability to represent and reason about the mental states of other agents. LLMs achieve parity with humans on some tasks (demonstrating that language statistics can in principle underpin mentalistic reasoning) but lag behind in others, suggesting that humans may rely on additional mechanisms. Second, I evaluate LLMs using the Turing test, which measures a machine's ability to imitate humans in a multi-turn social interaction. One model achieves a 50% pass rate, meaning participants are at chance in distinguishing it from a human. Collectively, the results suggest that LLMs simulate many aspects of our social intelligence, but by mechanisms that are potentially quite different from the ones that underpin human social cognition.
- 일반주제명
- Psychology
- 일반주제명
- Language
- 일반주제명
- Behavioral psychology
- 일반주제명
- Cognitive psychology
- 키워드
- Theory of mind
- 키워드
- Turing test
- 기타저자
- University of California, San Diego Cognitive Science
- 기본자료저록
- Dissertations Abstracts International. 86-03A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384067948
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a150
■1001 ▼aJones, Cameron Robert.
■24510▼aReading Minds: Social Intelligence and Large Language Models
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a184 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: A.
■500 ▼aAdvisor: Bergen, Benjamin.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aHuman social intelligence is one of the defining features of our species, however, its origins and mechanisms are not well understood. The advent of Large Language Models (LLMs)-which learn to produce text on the basis of statistical patterns in the distribution of words-both threaten the uniqueness of human social intelligence and promise opportunities to better understand it. In this dissertation, I evaluate the extent to which distributional information learned by LLMs allows them to approximate human behavior on tasks that appear to require social intelligence. First, I compare human and LLM responses in experiments designed to measure theory of mind-the ability to represent and reason about the mental states of other agents. LLMs achieve parity with humans on some tasks (demonstrating that language statistics can in principle underpin mentalistic reasoning) but lag behind in others, suggesting that humans may rely on additional mechanisms. Second, I evaluate LLMs using the Turing test, which measures a machine's ability to imitate humans in a multi-turn social interaction. One model achieves a 50% pass rate, meaning participants are at chance in distinguishing it from a human. Collectively, the results suggest that LLMs simulate many aspects of our social intelligence, but by mechanisms that are potentially quite different from the ones that underpin human social cognition.
■590 ▼aSchool code: 0033.
■650 4▼aPsychology
■650 4▼aLanguage
■650 4▼aBehavioral psychology
■650 4▼aCognitive psychology
■653 ▼aLarge Language Models
■653 ▼aSocial intelligence
■653 ▼aTheory of mind
■653 ▼aTuring test
■653 ▼aHuman social cognition
■690 ▼a0800
■690 ▼a0621
■690 ▼a0679
■690 ▼a0633
■690 ▼a0384
■71020▼aUniversity of California, San Diego▼bCognitive Science.
■7730 ▼tDissertations Abstracts International▼g86-03A.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160833▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
