{
 "id": "sh0416/humanextension",
 "owner": "sh0416",
 "name": "humanextension",
 "downloads": 99,
 "likes": 2,
 "last_modified": "2025-03-15T02:11:25.000Z",
 "stated_licence": "mit",
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 "citation": "@inproceedings{lee-etal-2024-exploring,\n    title = \"Exploring Language Model`s Code Generation Ability with Auxiliary Functions\",\n    author = \"Lee, Seonghyeon  and\n      Jang, Sanghwan  and\n      Jang, Seongbo  and\n      Lee, Dongha  and\n      Yu, Hwanjo\",\n    editor = \"Duh, Kevin  and\n      Gomez, Helena  and\n      Bethard, Steven\",\n    booktitle = \"Findings of the Association for Computational Linguistics: NAACL 2024\",\n    month = jun,\n    year = \"2024\",\n    address = \"Mexico City, Mexico\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.findings-naacl.181/\",\n    doi = \"10.18653/v1/2024.findings-naacl.181\",\n    pages = \"2836--2848\",\n    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",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "783701c9b72eb79ed6d81d97658a8ca53be43000",
 "stated_evidence": "https://huggingface.co/datasets/sh0416/humanextension/blob/783701c9b72eb79ed6d81d97658a8ca53be43000/README.md",
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}