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Xingsheng Han

dblp:435/0885 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 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.

Artificial intelligence
2 papers
Language models and text generation · 33% Efficient and distributed learning · 17% Learning paradigms · 17%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › large language model › large language model adaptation
supervised fine-tuning		 2.022026
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models · ACL (1) 2026
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning · ACL (1) 2026
Machine learning › Transfer learning and domain adaptation
fine-tuning		 1.012026
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models · ACL (1) 2026
Machine learning › Trustworthy machine learning
learning with incomplete data		 1.012026
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models · ACL (1) 2026
Machine learning › Efficient and distributed learning
parameter-efficient fine-tuning		 1.012026
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning · ACL (1) 2026
Machine learning › Learning paradigms › incremental learning
parameter isolation		 1.012026
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning · ACL (1) 2026

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

systematic study · 1.0fine-tuning analysis · 1.0
YearPublicationVenuePosition
2026 Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
abstract
Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Zekai Lin, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Bob Simons, Minlong Peng
 ACL (1)4
2026 Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
abstract
Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Haibo Shi, Minlong Peng, Flora D. Salim
 ACL (1)5