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Xin Zhang 0099

dblp:76/1584-99 · DBLP profile ↗
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6ranked-venue papers
0as first author
6since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 6 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
3 papers
Program synthesis and code generation · 46% Software maintenance and evolution · 23% Compilers and program optimization · 23%
Artificial intelligence
3 papers
Language models and text generation · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program synthesis and code generation › code generation with language models
repository-level code generation
1.722025
EpiCoder: Encompassing Diversity and Complexity in Code Generation · ICML 2025
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation · ACL (1) 2025
Natural language and speech › Language models and text generation
instruction tuning
1.622025
MAIN: Mutual Alignment Is Necessary for instruction tuning · EMNLP 2025
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning · ACL (1) 2024
Natural language and speech › Language models and text generation
code language models
1.022025
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning · ACL (1) 2024
EpiCoder: Encompassing Diversity and Complexity in Code Generation · ICML 2025
Software maintenance and evolution › program comprehension
code comprehension
0.912025
Teaching Your Models to Understand Code via Focal Preference Alignment · EMNLP 2025
Compilers and program optimization
code generation
0.912025
EpiCoder: Encompassing Diversity and Complexity in Code Generation · ICML 2025

Methods — techniques the papers use, named apart from their topics

fine-tuning · 1.7feature tree synthesis · 1.7instruction tuning · 1.6large language model · 0.9focal preference alignment · 0.9
YearPublicationVenuePosition
2025 FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation
abstract
Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Wei Li 0232, Xin Zhang 0099, Zhongxin Guo, Shaoguang Mao, Wen Luo 0001, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li
ACL (1)2
2025 Teaching Your Models to Understand Code via Focal Preference Alignment
abstract
Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.
Jie Wu 0001, Haoling Li, Xin Zhang 0099, Xiao Liu 0029, Yangyu Huang, Zuchao Li, Ruihang Chu, Yujiu Yang 0001, Scarlett Li
EMNLP3
2025 MAIN: Mutual Alignment Is Necessary for instruction tuning
abstract
Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.
Fanyi Yang, Xin Zhang 0099, Haoyu Liu 0002, Xixin Cao, Yuefeng Zhan, Hao Sun 0015, Feng Sun 0008, Qi Zhang 0066
EMNLP3
2025 EpiCoder: Encompassing Diversity and Complexity in Code Generation
abstract
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features derived from high-level abstractions of code. The feature tree is constructed from raw data and refined iteratively to increase the quantity and diversity of the extracted features, which captures and recognizes more complex patterns and relationships within the code. By adjusting the depth and breadth of the sampled subtrees, our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios. We fine-tuned widely-used base models to obtain EpiCoder series, achieving state-of-the-art performance on multiple benchmarks at both the function and file levels. In particular, empirical evidence indicates that our approach shows significant potential in the synthesizing of repository-level code data. Our code and data are publicly available.
Yaoxiang Wang, Haoling Li, Xin Zhang 0099, Jie Wu 0001, Xiao Liu 0029, Wenxiang Hu, Zhongxin Guo, Yangyu Huang, Yujiu Yang 0001, Jinsong Su, Qi Chen 0009, Scarlett Li
ICML3
2025 Instantly Learning Preference Alignment via In-context DPO
abstract
Feifan Song, Yuxuan Fan, Xin Zhang, Peiyi Wang, Houfeng Wang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
Feifan Song 0001, Yuxuan Fan, Xin Zhang 0099, Peiyi Wang, Houfeng Wang
NAACL (Long Papers)3
2024 WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning
abstract
Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
Zhaojian Yu, Xin Zhang 0099, Yangyu Huang, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin
ACL (1)2