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

Jonathan Tonglet

ACL 2026×2多模态评测与基准效率与系统数据与预训练
新星分新星
74
一作贡献60
核心/参与0
方向广度8
合作网络6
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

Protecting multimodal large language models against misleading visualizations

Jonathan Tonglet · Tinne Tuytelaars · Marie-Francine Moens · Iryna Gurevych

摘要

Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understanding has accelerated massively, with steady improvements on standard benchmarks. However, for MLLMs to be reliable, the...

多模态效率与系统评测与基准OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

Is this chart lying to me? Automating the detection of misleading visualizations

Jonathan Tonglet · Jan Zimny · Tinne Tuytelaars · Iryna Gurevych

摘要

Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccurate conclusions. Prior work has shown that both humans and multimodal large language models (MLLMs) are frequently d...

多模态评测与基准数据与预训练OpenReview 项目页

常见合作者