@inproceedings{gupta-etal-2025-codescm,
    title = "{C}ode{SCM}: Causal Analysis for Multi-Modal Code Generation",
    author = "Gupta, Mukur  and
      Bhatt, Noopur  and
      Jana, Suman",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.345/",
    pages = "6779--6793",
    ISBN = "979-8-89176-189-6",
    abstract = "In this paper, we propose CodeSCM, a Structural Causal Model (SCM) for analyzing multi-modal code generation using large language models (LLMs). By applying interventions to CodeSCM, we measure the causal effects of different prompt modalities, such as natural language, code, and input-output examples, on the model. CodeSCM introduces latent mediator variables to separate the code and natural language semantics of a multi-modal code generation prompt. Using the principles of Causal Mediation Analysis on these mediators we quantify direct effects representing the model{'}s spurious leanings. We find that, in addition to natural language instructions, input-output examples significantly influence code generation."
}