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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)
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Open Access Gold (Paid Option)
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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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Behavioral Science ; Hugging Face ; Large Language Models; Cognitive Reflection; Ai
ISSN (print) / ISBN 1554-351X
e-ISSN 1554-3528
Journal Behavior research methods
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Non-patent literature Publications
Institute(s) Institute of AI for Health (AIH)
Grants Academy of Finland (AKA)
Swiss Science Foundation
Schweizerischer Nationalfonds zur Frderung der Wissenschaftlichen Forschung