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

Yuting Huang

ACL 2026×2社会与人本检索与知识多模态效率与系统长上下文与架构
新星分新星
76
一作贡献60
核心/参与0
方向广度10
合作网络9
2 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
一作ACL 2026Findings· Findings of ACL

" I Don’t Know What to Say": A Fact-Filling Questionnaire Method to Help Non-Experts Talk to L egal AI Assistant

Yuting Huang · Yiquan Wu · Meitong Guo · Ang Li · Xiaozhong Liu · Keting Yin · Fei Wu · Kun Kuang

摘要

Artificial intelligence has become increasingly prevalent in the legal domain. However, LegalAI systems often struggle with vague user queries that lack essential legal details, leading to suboptimal performance in practical applications. To address this challenge, we propose FactFiller, a novel...

检索与知识社会与人本多模态OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

S plit T hen M erge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks

Yuting Huang · Jiawen Zhang · Yiquan Wu · Yinghao Hu · Fei Wu · Kun Kuang

摘要

Large language models have demonstrated strong performance on general-purpose tasks but often fail to satisfy the accuracy requirements of knowledge-intensive domains such as law, medicine, and finance. Complex domain-specific generation is inherently compositional, involving multiple atomic skil...

效率与系统社会与人本长上下文与架构OpenReview 项目页

常见合作者