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Hussain, Z.* ; Binz, M. ; Mata, R.* ; Wulff, D.U.*

A tutorial on open-source large language models for behavioral science.

Behav. Res. Methods, DOI: 10.3758/s13428-024-02455-8 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucial advantage for use in behavioral science. To help researchers harness the promise of LLMs, this tutorial offers a primer on the open-source Hugging Face ecosystem and demonstrates several applications that advance conceptual and empirical work in behavioral science, including feature extraction, fine-tuning of models for prediction, and generation of behavioral responses. Executable code is made available at github.com/Zak-Hussain/LLM4BeSci.git . Finally, the tutorial discusses challenges faced by research with (open-source) LLMs related to interpretability and safety and offers a perspective on future research at the intersection of language modeling and behavioral science.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Behavioral Science ; Hugging Face ; Large Language Models; Cognitive Reflection; Ai
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1554-351X
e-ISSN 1554-3528
Zeitschrift Behavior research methods
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Institut(e) Human-Centered AI (HCA)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540011-001
Förderungen Academy of Finland (AKA)
Swiss Science Foundation
Schweizerischer Nationalfonds zur Frderung der Wissenschaftlichen Forschung
DOI 10.3758/s13428-024-02455-8
WOS ID 001291249000002
Scopus ID 85201323351
PubMed ID 39147947
Erfassungsdatum 2024-10-01
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