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sh0416/humanextension

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Citation text from the card

@inproceedings{lee-etal-2024-exploring,
    title = "Exploring Language Model`s Code Generation Ability with Auxiliary Functions",
    author = "Lee, Seonghyeon  and
      Jang, Sanghwan  and
      Jang, Seongbo  and
      Lee, Dongha  and
      Yu, Hwanjo",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-naacl.181/",
    doi = "10.18653/v1/2024.findings-naacl.181",
    pages = "2836--2848",
    abstract = "Auxiliary function is a helpful component to improve language model`s code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work, we comprehensively evaluate the ability to utilize auxiliary functions encoded in recent code-pretrained language models. First, we construct a human-crafted evaluation set, called HumanExtension, which contains examples of two functions where one function assists the other.With HumanExtension, we design several experiments to examine their ability in a multifaceted way. Our evaluation processes enable a comprehensive understanding of including auxiliary functions in the prompt in terms of effectiveness and robustness. An additional implementation style analysis captures the models' various imple

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dataset_licence({ "id": "sh0416/humanextension" })
signal: "permissive"
stated_licence: "mit"
sources: 0