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<p>Natural language is a primary medium for expressing thoughts and emotions, making text analysis a vital tool in psychological research. It enables insights into personality traits, mental health, and sentiment in interpersonal communication. Traditional approaches – such as human coding, dictionary-based methods, or training models from scratch – often suffer from limitations, including inefficiency, incomplete coverage, or high data requirements. This tutorial introduces the pretrain-finetune paradigm, a transformative approach in text analysis and natural language processing (NLP) that leverages large pretrained language models. Unlike conventional methods, this paradigm enables efficient fine-tuning even with limited labeled data, making it particularly valuable for social science research where annotated samples are scarce. Our tutorial offers a comprehensive introduction to the pretrain-finetune framework, beginning with core concepts of pretraining and fine-tuning, followed by hands-on exercises with real-world applications, the introduction of <Emphasis FontCategory="NonProportional">finetuneR</Emphasis>, an R package developed to make these methods accessible to R users, and concluding with a discussion of common misconceptions in existing resources and best practices. We demonstrate its effectiveness across diverse tasks, including multi-class classification and regression, showing its advantages over traditional methods, feature extraction-based approaches, and GPT-based strategies. By emphasizing its efficiency, accessibility, and superior performance, this tutorial aims to encourage broader adoption of the pretrain-finetune paradigm in psychological and behavioral research.</p>

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A tutorial on fine-tuning pretrained language models: Applications in social and behavioral science research

  • 期刊论文

  • 发表日期：

    2025年11月5日

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  • Yu Wang,
  • Wen Qu

摘要

Natural language is a primary medium for expressing thoughts and emotions, making text analysis a vital tool in psychological research. It enables insights into personality traits, mental health, and sentiment in interpersonal communication. Traditional approaches – such as human coding, dictionary-based methods, or training models from scratch – often suffer from limitations, including inefficiency, incomplete coverage, or high data requirements. This tutorial introduces the pretrain-finetune paradigm, a transformative approach in text analysis and natural language processing (NLP) that leverages large pretrained language models. Unlike conventional methods, this paradigm enables efficient fine-tuning even with limited labeled data, making it particularly valuable for social science research where annotated samples are scarce. Our tutorial offers a comprehensive introduction to the pretrain-finetune framework, beginning with core concepts of pretraining and fine-tuning, followed by hands-on exercises with real-world applications, the introduction of finetuneR, an R package developed to make these methods accessible to R users, and concluding with a discussion of common misconceptions in existing resources and best practices. We demonstrate its effectiveness across diverse tasks, including multi-class classification and regression, showing its advantages over traditional methods, feature extraction-based approaches, and GPT-based strategies. By emphasizing its efficiency, accessibility, and superior performance, this tutorial aims to encourage broader adoption of the pretrain-finetune paradigm in psychological and behavioral research.