activity
20192026
most citedMuRIL: Multilingual Representations for Indian Languages

160 citations · 288 across the 25 of their papers we have counts for

collaborators

26 papers

cs.CL2026

Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

Parinthapat Pengpun, Simran Khanuja, Graham Neubig

Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily thr…

cs.AI2026

NormViz: A Benchmark and Framework for Grounding Multimodal Reasoning in Global Cultures

Akhila Yerukola, Fabrice Y Harel-Canada, Simran Khanuja +5

AI systems are used worldwide, but they struggle to serve the needs of culturally diverse populations. Prior work on cultural understanding evaluates AI systems on text-only settin…

cs.CL2026

Steering LLMs for Culturally Localized Generation

Simran Khanuja, Hongbin Liu, Shujian Zhang +4

LLMs are deployed globally, yet produce responses biased towards cultures with abundant training data. Existing cultural localization approaches such as prompting or post-training…

cs.CL2026

Linguistically Informed Evaluation of Multilingual ASR for African Languages

Fei-Yueh Chen, Lateef Adeleke, C. M. Downey

Word Error Rate (WER) mischaracterizes ASR models' performance for African languages by combining phonological, tone, and other linguistic errors into a single lexical error. By co…

cs.CL2025

MERLIN: A Testbed for Multilingual Multimodal Entity Recognition and Linking

Sathyanarayanan Ramamoorthy, Vishwa Shah, Simran Khanuja +5

This paper introduces MERLIN, a novel testbed system for the task of Multilingual Multimodal Entity Linking. The created dataset includes BBC news article titles, paired with corre…

cs.CL2025

Grounding Multilingual Multimodal LLMs With Cultural Knowledge

Jean de Dieu Nyandwi, Yueqi Song, Simran Khanuja +1

Multimodal Large Language Models excel in high-resource settings, but often misinterpret long-tail cultural entities and underperform in low-resource languages. To address this gap…