@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",
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 implementation patterns when they access the auxiliary function. Through this analysis, we discover the models{'} promising ability to utilize auxiliary functions including their self-improving behavior by implementing the two functions step-by-step. However, our analysis also reveals the model{'}s underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models. We release our code and dataset to facilitate this research direction.",
}
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<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 implementation patterns when they access the auxiliary function. Through this analysis, we discover the models’ promising ability to utilize auxiliary functions including their self-improving behavior by implementing the two functions step-by-step. However, our analysis also reveals the model’s underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models. We release our code and dataset to facilitate this research direction.</abstract>
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%0 Conference Proceedings
%T Exploring Language Model’s Code Generation Ability with Auxiliary Functions
%A Lee, Seonghyeon
%A Jang, Sanghwan
%A Jang, Seongbo
%A Lee, Dongha
%A Yu, Hwanjo
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lee-etal-2024-exploring
%X 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 implementation patterns when they access the auxiliary function. Through this analysis, we discover the models’ promising ability to utilize auxiliary functions including their self-improving behavior by implementing the two functions step-by-step. However, our analysis also reveals the model’s underutilized behavior to call the auxiliary function, suggesting the future direction to enhance their implementation by eliciting the auxiliary function call ability encoded in the models. We release our code and dataset to facilitate this research direction.
%U https://aclanthology.org/2024.findings-naacl.181
%P 2836-2848
Markdown (Informal)
[Exploring Language Model’s Code Generation Ability with Auxiliary Functions](https://aclanthology.org/2024.findings-naacl.181) (Lee et al., Findings 2024)
ACL