@inproceedings{twist-etal-2026-study,
    title = "A Study of {LLM}s' Preferences for Libraries and Programming Languages",
    author = "Twist, Lukas  and
      Zhang, Jie M.  and
      Harman, Mark  and
      Syme, Don  and
      Noppen, Joost  and
      Yannakoudakis, Helen  and
      Nauck, Detlef",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.15/",
    pages = "331--351",
    ISBN = "979-8-89176-395-1",
    abstract = "Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight diverse LLMs.We observe a strong tendency to overuse widely adopted libraries such as NumPy; in up to 45{\%} of cases, this usage is not required and deviates from the ground-truth solutions.The LLMs we study also show a significant preference toward Python as their default language.For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58{\%} of cases, and Rust is not used once.These results highlight how LLMs prioritise familiarity and popularity over suitability and task-specific optimality;underscoring the need for targeted fine-tuning, data diversification, and evaluation benchmarks that explicitly measure language and library selection fidelity."
}