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William A Held

Datos Biográficos

ID3452913
NOMBREWilliam A Held
NOMBRESWilliam A
APELLIDOHeld
FIRMAHELD W A
AFILIACIONESGeorgia Institute of Technology
VERIFICADONo
TOTAL DE OBRAS1
TOTAL DE CITAS49
TOTAL COMO AUTOR1
TOTAL COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2023
AÑO MÁS RECIENTE DE PUBLICACIÓN2023
ÍNDICE H1
  • Can Large Language Models Transform Computational Social Science

    Open Access•Caleb Ziems, William A Held et al.•ARTICLE•Computational Linguistics•2023•Citada por: 49•Referencias: 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•Citada por: 49•Referencias: 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•Citada por: 49•Referencias: 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 obras) · Computational and Text Analysis Methods (1 obras) · Natural Language Processing Techniques (1 obras) · Topic Modeling (1 obras)

Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae