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

Jia Chen

ACL 2026×2评测与基准智能体与工具推理效率与系统代码
新星分贡献者
38
一作贡献0
核心/参与10
方向广度10
合作网络6
0 一作 · 2 篇 · ACL 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

2
作者位次 #3/5ACL 2026Poster· Natural Language Processing

Every Response Counts: Quantifying Uncertainty of LLM -based Multi-Agent Systems through Tensor Decomposition

Tiejin Chen · Huaiyuan Yao · Jia Chen · Evangelos E. Papalexakis · Hua Wei

摘要

While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies. Existing Uncertainty Quantification methods,...

智能体与工具推理评测与基准OpenReview 项目页
作者位次 #4/6ACL 2026Findings· Findings of ACL

Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources

Tiejin Chen · Xiaoou Liu · Longchao Da · Jia Chen · Evangelos E. Papalexakis · Hua Wei

摘要

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains. However, the reliability of responses from LLMs remains a question. Uncertainty quantification (UQ) of LLMs is crucial for ensuring their reliability, especially in areas such as healthcare. Existing UQ...

效率与系统评测与基准代码OpenReview 项目页

常见合作者