@inproceedings{bhattacharya-gupta-2025-selective,
    title = "Selective Shot Learning for Code Explanation",
    author = "Bhattacharya, Paheli  and
      Gupta, Rishabh",
    editor = "Dhole, Kaustubh  and
      Clinciu, Miruna",
    booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
    month = jul,
    year = "2025",
    address = "Vienna, Austria and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.gem-1.12/",
    pages = "151--160",
    ISBN = "979-8-89176-261-9",
    abstract = "Code explanation plays a crucial role in the software engineering domain, aiding developers in grasping code functionality efficiently. Recent work shows that the performance of LLMs for code explanation improves in a few-shot setting, especially when the few-shot examples are selected intelligently. State-of-the-art approaches for such Selective Shot Learning (SSL) include token-based and embedding-based methods. However, these SSL approaches have been evaluated on proprietary LLMs, without much exploration on open-source Code-LLMs. Additionally, these methods lack consideration for programming language syntax. To bridge these gaps, we present a comparative study and propose a novel SSL method (SSL{\_}ner) that utilizes entity information for few-shot example selection. We present several insights and show the effectiveness of SSL{\_}ner approach over state-of-the-art methods across two datasets. To the best of our knowledge, this is the first systematic benchmarking of various few-shot examples selection approaches using open-source Code-LLMs for the code explanation task."
}