Researchers have developed OmniScore, a family of deterministic learned metrics designed to evaluate generative text as an alternative to Large Language Models (LLMs) used as judges. OmniScore leverages small parameter models (<1B) and was trained on approximately 564,000 synthetic instances across 107 languages, then evaluated using 8,617 manually annotated instances. It approximates LLM-judge behavior while offering low latency and consistency for various evaluation settings like reference-based and source-grounded assessments in tasks like QA, translation, and summarization. Why it matters: This development provides a practical, scalable, and reproducible method for multilingual generative text evaluation, addressing key limitations of LLM-as-a-judge approaches and offering significant benefits for AI development in linguistically diverse regions.
Hamad Bin Khalifa University (HBKU) has released Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform, built entirely at QCRI. The core of Fanar 2.0 is Fanar-27B, which was continually pre-trained from a Gemma-3-27B backbone using 120 billion high-quality tokens and only 256 NVIDIA H100 GPUs. Fanar 2.0 includes capabilities like FanarGuard, Aura, Oryx, Fanar-Sadiq, Fanar-Diwan, and FanarShaheen for moderation, speech recognition, vision understanding, Islamic content, poetry generation, and translation. Why it matters: This shows that sovereign, resource-constrained AI development in the Arabic language is possible, producing competitive systems in the region.
The Qatar Computing Research Institute (QCRI) has released SpokenNativQA, a multilingual spoken question-answering dataset for evaluating LLMs in conversational settings. The dataset contains 33,000 naturally spoken questions and answers across multiple languages, including low-resource and dialect-rich languages. It aims to address the limitations of text-based QA datasets by incorporating speech variability, accents, and linguistic diversity. Why it matters: This benchmark enables more robust evaluation of LLMs in speech-based interactions, particularly for Arabic dialects and other low-resource languages.
Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) introduced Fanar, an Arabic-centric multimodal generative AI platform featuring the Fanar Star (7B) and Fanar Prime (9B) Arabic LLMs. These models were trained on nearly 1 trillion tokens and are designed to address different prompts through a custom orchestrator. Fanar includes a customized Islamic RAG system, a Recency RAG, bilingual speech recognition, and an attribution service for content verification, sponsored by Qatar's Ministry of Communications and Information Technology. Why it matters: The platform signifies a major step towards sovereign AI development in Qatar, providing advanced Arabic language capabilities and addressing regional needs.
Qatar Computing Research Institute (QCRI) has developed NatiQ, an end-to-end text-to-speech (TTS) system for Arabic utilizing encoder-decoder architectures. The system employs Tacotron-based models and Transformer models to generate mel-spectrograms, which are then synthesized into waveforms using vocoders like WaveRNN, WaveGlow, and Parallel WaveGAN. Trained on in-house speech data featuring a neutral male voice (Hamza) and an expressive female voice (Amina), NatiQ achieves a Mean Opinion Score (MOS) of 4.21 and 4.40, respectively. Why it matters: This research advances Arabic language technology, providing high-quality TTS synthesis that can enhance accessibility and usability of digital content for Arabic speakers.
The Qatar Computing Research Institute (QCRI) has released QASR, a 2,000-hour transcribed Arabic speech corpus collected from Aljazeera news broadcasts. The dataset features multi-dialect speech sampled at 16kHz, aligned with lightly supervised transcriptions and linguistically motivated segmentation. QCRI also released a 130M word dataset to improve language model training. Why it matters: QASR enables new research in Arabic speech recognition, dialect identification, punctuation restoration, and other NLP tasks for spoken data.
CSAIL (MIT's Computer Science and Artificial Intelligence Laboratory) and the Qatar Computing Research Institute (QCRI) have announced a new research collaboration. This partnership aims to foster joint research projects and facilitate knowledge exchange between the two prominent AI research institutions. The collaboration is expected to leverage expertise from both sides in various AI domains. Why it matters: This partnership signifies a strengthening of AI research ties between a leading US institution and a major Middle Eastern research hub, potentially driving advancements relevant to the region and the broader AI field.
Hamad Bin Khalifa University (HBKU) and the Qatar AI Center (QAI) at Qatar Computing Research Institute (QCRI) are partnering to advance AI research, policy, and capability building. The collaboration aims to support Qatar's National AI Strategy through joint research projects, knowledge exchange, and the development of AI talent. Specific areas of focus include AI ethics, healthcare, and autonomous systems. Why it matters: The partnership strengthens Qatar's AI ecosystem by combining HBKU's academic expertise with QAI's applied research capabilities, accelerating innovation and addressing key challenges in the region.