activity
20212026
most citedJais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

23 citations · 23 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Jais 2: A Family of Arabic-Centric Open Large Language Models

Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim +57

Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong p…

cs.IR2026

PETRA: Transforming Web Text for Petroleum-Engineering Domain Adaptation

Kirill Dubovikov, Omar El Mansouri, Hachem Madmoun +12

Petroleum-engineering search exposes a supervision gap for strong general retrievers: relevant evidence exists in public web text, but domain relevance labels are scarce. To addres…

cs.CL2025

Llama-3-Nanda-10B-Chat: An Open Generative Large Language Model for Hindi

Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das +27

Developing high-quality large language models (LLMs) for moderately resourced languages presents unique challenges in data availability, model adaptation, and evaluation. We introd…

cs.CL2024

Bilingual Adaptation of Monolingual Foundation Models

Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan +19

We present an efficient method for adapting a monolingual Large Language Model (LLM) to another language, addressing challenges of catastrophic forgetting and tokenizer limitations…

cs.CL2023★ 23 cited

Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models

Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +29

We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-…

cs.CL2021

Probing for Bridging Inference in Transformer Language Models

Onkar Pandit, Yufang Hou

We probe pre-trained transformer language models for bridging inference. We first investigate individual attention heads in BERT and observe that attention heads at higher layers p…