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2505.14597
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Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals
20 May 2025
Xianzhen Luo
Qingfu Zhu
Zhiming Zhang
Mingzheng Xu
Tianhao Cheng
Yixuan Wang
Zheng Chu
Shijie Xuyang
Zhiyuan Ma
YuanTao Fan
Wanxiang Che
AAML
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Papers citing
"Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals"
3 / 3 papers shown
Title
RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
Feng Lin
Dong Jae Kim
Zhiyu Li
Jinqiu Yang
Tse-Husn
Chen
AAML
317
6
0
28 Mar 2025
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo
Minh Chien Vu
Jenny Chim
Han Hu
Wenhao Yu
...
David Lo
Daniel Fried
Xiaoning Du
H. D. Vries
Leandro von Werra
571
358
0
22 Jun 2024
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
International Conference on Learning Representations (ICLR), 2023
Ziyang Luo
Can Xu
Lu Wang
Qingfeng Sun
Xiubo Geng
Wenxiang Hu
Chongyang Tao
Jing Ma
Qingwei Lin
Daxin Jiang
ELM
SyDa
ALM
682
838
0
14 Jun 2023
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