Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning

Authors

Zeynab Samei (ORCID: https://orcid.org/0000-0002-6802-5090)
Maria Korobeynikova
Maël Kubli (ORCID: https://orcid.org/0000-0002-5592-9648)
Shirin Dehghani
Fabrizio Gilardi (ORCID: https://orcid.org/0000-0002-0635-3048)
Meysam Alizadeh (ORCID: https://orcid.org/0000-0001-6696-6471)
Mohammadmasiha Zahedivafa
Juan Diego Bermeo (ORCID: https://orcid.org/0009-0000-6874-4848)

Institutions

Allameh Tabataba'i University (IR)
University of Zurich (CH)
Institute for Research in Fundamental Sciences (IR)
Iran University of Science and Technology (IR)

Publication Details

Journal: Journal of Computational Social Science
Published: 2024-12-18
DOI: https://doi.org/10.1007/s42001-024-00345-9
Citations: 43
Primary Topic: Topic Modeling
Type: article
Field-Weighted Citation Impact: 14.94

Funders

European Commission
Universität Zürich
H2020 European Research Council

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article

Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning

Zeynab Samei, Maria Korobeynikova, Maël Kubli, Shirin Dehghani et al.

Dec 18, 2024

43 citations

Journal of Computational Social Science

Topic Modeling

14.94

article

Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning

Zeynab Samei, Maria Korobeynikova, Maël Kubli, Shirin Dehghani, Fabrizio Gilardi, Meysam Alizadeh, Mohammadmasiha Zahedivafa, Juan Diego Bermeo

December 18, 2024article en

43 citations

Abstract

No abstract available for this paper.

Journal of Computational Social ScienceVol. 8(1)

Allameh Tabataba'i University (IR), University of Zurich (CH), Institute for Research in Fundamental Sciences (IR), Iran University of Science and Technology (IR)

European Commission, Universität Zürich, H2020 European Research Council

Quality Education

Openalex Percentile: Top 1%

Topic Modeling

14.94

DOI Open Access PDF OpenAlex

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