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  1. Home
  3. Open-source Llms For Text Annotation: A Practical Guide For Model Setting And Fine-tuning.
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  3. Open-source Llms For Text Annotation: A Practical Guide For Model Setting And Fine-tuning.

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

Meysam Alizadeh1, Maël Kubli1, Zeynab Samei2

  • 1Department of Political Science, University of Zurich, 8050 Zurich, Switzerland.

Journal of Computational Social Science
|December 23, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Fine-tuning open-source Large Language Models (LLMs) significantly boosts performance on political science text classification tasks, outperforming zero-shot models and offering a practical alternative to few-shot training.

Keywords:
ChatGPTFLANLLMsLLaMANLPOpen sourceText annotation

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Area of Science:

  • Political Science
  • Computational Social Science
  • Natural Language Processing

Background:

  • Open-source Large Language Models (LLMs) are increasingly used for text analysis.
  • Scholars require guidance on LLM performance for specific political science tasks.
  • Establishing benchmarks for LLM effectiveness in social science research is crucial.

Purpose of the Study:

  • To evaluate the performance of open-source LLMs in political science text classification.
  • To compare zero-shot and fine-tuned LLM capabilities on tasks like stance, topic, and relevance.
  • To provide a benchmark for LLM effectiveness and inform scholarly decision-making.

Main Methods:

  • Assessed open-source LLMs using both zero-shot and fine-tuned approaches.
  • Utilized news articles and tweets datasets for text annotation tasks.
  • Compared fine-tuning against few-shot training with limited annotated data.

    • Main Results:

    • Fine-tuning enhances open-source LLM performance, matching or exceeding zero-shot GPT-3.5 and GPT-4.
    • Fine-tuned open-source LLMs still lag behind fine-tuned GPT-3.5.
    • Fine-tuning is more effective than few-shot training with modest data.

    Conclusions:

    • Fine-tuned open-source LLMs are suitable for diverse text annotation applications in political science.
    • The study provides a practical benchmark for LLM performance in social science research.
    • A Python notebook is available to assist researchers in applying LLMs for text annotation.