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William A Held

Biographic Data

ID3452913
NAMEWilliam A Held
GIVEN NAMESWilliam A
FAMILY NAMEHeld
SIGNATUREHELD W A
AFFILIATIONSGeorgia Institute of Technology
VERIFIEDNo
TOTAL WORKS1
TOTAL CITATIONS49
AUTHOR COUNT1
EDITOR COUNT0
FIRST PUBLICATION YEAR2023
LATEST PUBLICATION YEAR2023
H-INDEX1
  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Cited by: 49•References: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Cited by: 49•References: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Cited by: 49•References: 27

    Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompti…

Bootstrapping (finance (1 works) · Computational and Text Analysis Methods (1 works) · Natural Language Processing Techniques (1 works) · Topic Modeling (1 works)

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