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

Yonggang Zhang

ACL 2026×2ICLR 2026×2检索与知识强化学习与后训练评测与基准效率与系统多模态
新星分贡献者
73
一作贡献30
核心/参与17
方向广度13
合作网络12
1 一作 · 4 篇 · ACL 2026 / ICLR 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

4
一作ICLR 2026Poster· Applications / Language, Speech and Dialog

Enhancing LLMs for Knowledge Base Question Answering by Chain-of-Decomposition

Yonggang Zhang · Jianqi Gao · Jie Lu

检索与知识多模态OpenReview
作者位次 #4/9ACL 2026Poster· Natural Language Processing

Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search

Tao Feng · Xinke Jiang · Xinyan Hu · Yonggang Zhang · Zhen Tao · Wentao Zhang · Boyang Liu · Wenhao Jiang · Chao Wu

摘要

Agentic search augments large language models (LLMs) with external knowledge through reinforcement learning. However, existing approaches suffer from blind reliance on noisy retrieval and hallucination when both parametric and external knowledge fail—reflecting a lack of calibration regarding the...

检索与知识可解释性强化学习与后训练OpenReview 项目页
作者位次 #7/7ACL 2026Poster· Natural Language Processing

Generating then Refining for Reliable Knowledge Base Question Answering

Jianqi Gao · Hang Yu · Jian Cao · Ranran Bu · Jinghua Tang · Nengjun Zhu · Yonggang Zhang

摘要

Knowledge Base Question Answering (KBQA) aims to retrieve accurate answers to natural language queries by retrieving and reasoning over large-scale structured knowledge bases (KBs). Advanced semantic parsing-based methods promoted by large language models (LLMs) demonstrate superior performance b...

检索与知识推理评测与基准OpenReview 项目页

常见合作者