paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
HX

Hao Xu

ACL 2026×4AAAI 2026×2检索与知识评测与基准效率与系统多模态推理
新星分资深
33
一作贡献30
核心/参与0
方向广度13
合作网络0
1 一作 · 6 篇 · ACL 2026 / AAAI 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

6
一作ACL 2026Poster· Natural Language Processing

Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval

Hao Xu · Rite Bo · Fausto Giunchiglia · Yingji Li · Rui Song

摘要

Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrati...

检索与知识效率与系统评测与基准OpenReview 项目页
作者位次 #5/5ACL 2026Poster· Natural Language Processing

Enhancing Multimodal Large Language Models for A ncient C hinese Character Evolution Analysis via Glyph-Driven Fine-Tuning

Rui Song · Lida Shi · Ruihua Qi · Yingji Li · Hao Xu

摘要

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can...

评测与基准推理多模态OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

ACSE : An Ancient Character Semantic-Aware Embedding for Large Language Models

Zhihan Zhou · Daqian Shi · Lida Shi · Rui Song · Peiqiang Qiu · Xiaolei Diao · Hao Xu

摘要

Research on ancient Chinese language is of great significance for tracing Chinese history and civilization. In the field of large language models, studies on the pre-Qin excavated documents such as Oracle Bone Inscriptions, Bronze Inscriptions, and Bamboo Book of Chu remain insufficient. This is...

效率与系统检索与知识OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models

Rui Song · Yingji Li · Jian Li · Fausto Giunchiglia · Hao Xu

摘要

With the rapid development of Large Language Models (LLMs), In-Context Learning (ICL) has emerged as one of the universal paradigms for unleashing the capabilities of LLMs. However, LLMs are generally plagued by various biases in context example selection, which can distort the model’s prediction...

可解释性多模态检索与知识OpenReview 项目页

常见合作者