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20212026
most citedNL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

25 citations · 57 across the 28 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025

Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations

Patrick Amadeus Irawan, Ryandito Diandaru, Belati Jagad Bintang Syuhada +5

We introduce Entropy2Vec, a novel framework for deriving cross-lingual language representations by leveraging the entropy of monolingual language models. Unlike traditional typolog…

cs.CL2025

Language Surgery in Multilingual Large Language Models

Joanito Agili Lopo, Muhammad Ravi Shulthan Habibi, Tack Hwa Wong +6

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigat…

cs.AI2025

The Multilingual Divide and Its Impact on Global AI Safety

Aidan Peppin, Julia Kreutzer, Alice Schoenauer Sebag +13

Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small…

cs.CL2025

What Causes Knowledge Loss in Multilingual Language Models?

Maria Khelli, Samuel Cahyawijaya, Ayu Purwarianti +1

Cross-lingual transfer in natural language processing (NLP) models enhances multilingual performance by leveraging shared linguistic knowledge. However, traditional methods that pr…

cs.CL2025

Command A: An Enterprise-Ready Large Language Model

Team Cohere, :, Aakanksha +227

In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…

cs.CV2025

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…