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

Artur Kulmizev

ACL 2026×2可解释性效率与系统社会与人本评测与基准数据与预训练
新星分新星
67
一作贡献30
核心/参与12
方向广度10
合作网络9
1 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

Label and Explanation Variation in LLM -Based Annotation: a Case Study in Natural Language Inference

Artur Kulmizev · Erika Lombart · Patrick Watrin · Marie-Catherine de Marneffe

摘要

Large language models (LLMs) have shown considerable promise for annotation purposes, yet questions remain about their ability to capture human label variation (HLV) — genuine disagreement between annotators often observed across NLP tasks. Here, we investigate how label and explanation variation...

可解释性效率与系统社会与人本OpenReview 项目页
作者位次 #2/9ACL 2026Poster· Natural Language Processing

How Good is Your W ikipedia? Auditing Data Quality for Low-resource and Multilingual NLP

Kushal Tatariya · Artur Kulmizev · Wessel Poelman · Esther Ploeger · Marcel Bollmann · Johannes Bjerva · Jiaming Luo · Heather Lent · Miryam de Lhoneux

摘要

Wikipedia’s perceived high quality and broad language coverage have established it as a fundamental resource in NLP. However, in recent years, such assumptions of high quality have become the subject of scrutiny in low-resource and multilingual contexts. In this study, we subject the entirety of...

评测与基准数据与预训练多语言与语言学OpenReview 项目页

常见合作者