@inproceedings{li-etal-2026-example,
title = "Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming",
author = "Li, Yuhao and
Sun, Haifeng and
Zhang, Xuesong and
Yao, Shu and
Zheng, Haoyu and
Wang, Yvchuan and
Wang, Huazheng and
Zhuang, Zirui and
Qi, Qi and
Liao, Jianxin and
Wang, Jingyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1731/",
pages = "37311--37327",
ISBN = "979-8-89176-390-6",
abstract = "Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects{---}complexity, readability, and correctness{---}and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22{\%} accuracy improvement over baseline approaches. Code is available at [Anonymous Github](https://github.com/FireAndWin/ComboPrompt{\_}ExampleQualityMatters)."
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<abstract>Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at [Anonymous Github](https://github.com/FireAndWin/ComboPrompt_ExampleQualityMatters).</abstract>
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%0 Conference Proceedings
%T Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming
%A Li, Yuhao
%A Sun, Haifeng
%A Zhang, Xuesong
%A Yao, Shu
%A Zheng, Haoyu
%A Wang, Yvchuan
%A Wang, Huazheng
%A Zhuang, Zirui
%A Qi, Qi
%A Liao, Jianxin
%A Wang, Jingyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-example
%X Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at [Anonymous Github](https://github.com/FireAndWin/ComboPrompt_ExampleQualityMatters).
%U https://aclanthology.org/2026.acl-long.1731/
%P 37311-37327
Markdown (Informal)
[Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming](https://aclanthology.org/2026.acl-long.1731/) (Li et al., ACL 2026)
ACL
- Yuhao Li, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang, Huazheng Wang, Zirui Zhuang, Qi Qi, Jianxin Liao, and Jingyu Wang. 2026. Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 37311–37327, San Diego, California, United States. Association for Computational Linguistics.