metadata
license: mit
dataset_info:
- config_name: normal
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: entry_point
dtype: string
- name: entry_point_auxiliary
dtype: string
- name: test
dtype: string
splits:
- name: test
num_bytes: 293328
num_examples: 151
download_size: 130893
dataset_size: 293328
- config_name: normal-instruct
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: entry_point
dtype: string
- name: entry_point_auxiliary
dtype: string
- name: test
dtype: string
splits:
- name: test
num_bytes: 302780
num_examples: 151
download_size: 129722
dataset_size: 302780
- config_name: with_auxiliary
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: canonical_solution
dtype: string
- name: entry_point
dtype: string
- name: entry_point_auxiliary
dtype: string
- name: test
dtype: string
splits:
- name: test
num_bytes: 404289
num_examples: 151
download_size: 178426
dataset_size: 404289
- config_name: with_auxiliary-instruct
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: canonical_solution
dtype: string
- name: entry_point
dtype: string
- name: response_prefix_normal
dtype: string
- name: response_prefix_with_auxiliary
dtype: string
- name: entry_point_auxiliary
dtype: string
- name: test
dtype: string
splits:
- name: test
num_bytes: 722371
num_examples: 151
download_size: 300098
dataset_size: 722371
configs:
- config_name: normal
data_files:
- split: test
path: normal/test-*
- config_name: normal-instruct
data_files:
- split: test
path: normal-instruct/test-*
- config_name: with_auxiliary
data_files:
- split: test
path: with_auxiliary/test-*
- config_name: with_auxiliary-instruct
data_files:
- split: test
path: with_auxiliary-instruct/test-*
Related github repository: https://github.com/sh0416/humanextension
Also, please cite the following paper if you use this evaluation set in your experiments.
@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 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."
}
@inproceedings{lee-etal-2024-eliciting,
title = "Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation",
author = "Lee, Seonghyeon and
Kim, Suyeon and
Jang, Joonwon and
Chon, HeeJae and
Lee, Dongha and
Yu, Hwanjo",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.100/",
doi = "10.18653/v1/2024.findings-emnlp.100",
pages = "1840--1846",
abstract = "We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a function. We design several ways to provide auxiliary functions to the models by adding them to the query or providing a response prefix to incorporate the ability to utilize auxiliary functions with the instruction-following capability. Our experimental results show the effectiveness of combining the base models' auxiliary function utilization ability with the instruction following ability. In particular, the performance of adopting our approaches with the open-sourced language models surpasses that of the recent powerful language models, i.e., gpt-4o."
}