A Study of LLMs' Preferences for Libraries and Programming Languages
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Computer Science > Software Engineering
Title:A Study of LLMs' Preferences for Libraries and Programming Languages
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.
| Comments: | 21 pages, 10 tables, 3 figures. Accepted to Findings of ACL 2026 |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2503.17181 [cs.SE] |
| (or arXiv:2503.17181v4 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2503.17181
arXiv-issued DOI via DataCite
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| Journal reference: | Findings of the Association for Computational Linguistics: ACL 2026 |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-acl.15
DOI(s) linking to related resources
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Submission history
From: Lukas Twist [view email][v1] Fri, 21 Mar 2025 14:29:35 UTC (715 KB)
[v2] Mon, 21 Jul 2025 12:58:26 UTC (349 KB)
[v3] Wed, 8 Apr 2026 09:48:41 UTC (620 KB)
[v4] Thu, 4 Jun 2026 14:55:07 UTC (620 KB)
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