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

Junjie Wu

ACL 2026×2评测与基准检索与知识可解释性对齐与偏好多模态
新星分新星
77
一作贡献60
核心/参与0
方向广度10
合作网络12
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 2026Poster· Natural Language Processing

Situated Embedding Models for Context-Aware Dense Retrieval

Junjie Wu · Jiangnan Li · Yuqing Li · Lemao Liu · Liyan Xu · Jiwei Li · Dit-Yan Yeung · Jie Zhou · Mo Yu

摘要

Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to dependencies across the original document, contextual information is often essential for accurately interpreting each c...

评测与基准检索与知识可解释性OpenReview 项目页
一作ACL 2026Poster· ACL Industry Track

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM -as-a-Judge

Junjie Wu · Xuan Kan · Zihao He · Shunwen Tan · Bo Pan · Kaitai Zhang

摘要

Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-aJudges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is...

对齐与偏好多模态强化学习与后训练OpenReview 项目页

常见合作者