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

Sanchit Ahuja

ACL 2026ICML 2026评测与基准数据与预训练多语言与语言学对齐与偏好
新星分贡献者
45
一作贡献0
核心/参与10
方向广度11
合作网络12
0 一作 · 2 篇 · ACL 2026 / ICML 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 #4/37ICML 2026Poster· General Machine Learning / Evaluation

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

Mubashara Akhtar · Anka Reuel · Prajna Soni · Sanchit Ahuja · Pawan Sasanka Ammanamanchi · Ruchit Rawal · Vilém Zouhar · Srishti Yadav · Chenxi Whitehouse · Dayeon Ki · Jennifer Mickel · Leshem Choshen · Marek Šuppa · Jan Batzner · Jenny Chim · Jeba Sania · Yanan Long · Hossein A. Rahmani · Christina Knight · Yiyang Nan · Jyoutir Raj · Yu Fan · Shubham Singh · Subramanyam Sahoo · Eliya Habba · Usman Gohar · Siddhesh Pawar · Robert Scholz · Arjun Subramonian · Jingwei Ni · Mykel Kochenderfer · Sanmi Koyejo · Mrinmaya Sachan · Stella Biderman · Zeerak Talat · Avijit Ghosh · Irene Solaiman

评测与基准OpenReview
作者位次 #3/7ACL 2026Poster· Natural Language Processing

UPDESH : Synthesizing Grounded Instruction Tuning Data for 13 I ndic Languages

Pranjal A Chitale · Varun Gumma · Sanchit Ahuja · Prashant Kodali · Manan Uppadhyay · Deepthi Sudharsan · Sunayana Sitaram

摘要

Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multilingual and multicultural contexts is underexplored. We investigate bottom-up synthetic data generation using large o...

数据与预训练多语言与语言学对齐与偏好OpenReview 项目页

常见合作者