ArticleJournal of computational social science2025
Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning.
Article in Journal of computational social science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The shrinking landscape of linguistic diversity in the age of large language models.Nature human behaviour · 2026Article
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Attitudes and Willingness to Participate in Drug Clinical Trials Among Patients With Cancer: Multistage Qualitative Study.Journal of medical Internet research · 2026Article
- Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.Journal of medical Internet research · 2026Article
- Evaluating AI models for food and alcohol advertisement classification against human benchmarks.Scientific reports · 2026Article
- ViralMultiNet: A structure-aware multimodal framework for viral protein function prediction in wastewater surveillance.PloS one · 2026Article
- Towards scalable and reliable coding of semantic property norms: ChatGPT vs. an improved AC-PLT.Behavior research methods · 2025Article
- New opportunities and challenges for conservation evidence synthesis from advances in natural language processing.Conservation biology : the journal of the Society for Conservation Biology · 2025Article
- Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis and to establish a baseline performance benchmark that demonstrates the models' effectiveness. Specifically, we conduct an assessment of both zero-shot and fine-tuned LLMs across a range of text annotation tasks using news articles and tweets datasets. Our analysis shows that fine-tuning improves the performance of open-source LLMs, allowing them to match or even surpass zero-shot GPT Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-024-00345-9.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.